Keywords: Robotics, Autonomous robots, Adaptive control
Abstract: In this paper, a backstepping controller is presented for quadrotor system to follow a predefined trajectory without the knowledge of dynamic parameters in the control algorithms. By dividing the quadrotor systems into several subsystems, the idea of nominal control input is presented to modify the quadrotor input force and torque. Subsequently, the backstepping control technique is developed for the nominal controls with the adaptive laws to estimate unknown dynamic parameters. Both the mass and moment of inertia are coped with by using adaptive laws. The stability and tracking performance of the proposed closed-loop system are proved by using Lyapunov theorem that the tracking errors converge to the origin asymptotically. Numerical examples are presented to show the quadrotor system tracking a desired trajectory efficiently.

Keywords: Robotics, Control applications
Abstract: We propose a control architecture with the aim of providing a firm touch of a soft somatosensitive actuator (SSA) finger with an object. The two main components of the architecture are a reference tracking curvature controller and a force controller. The first one sets the finger in motion which is blocked if the finger comes into contact with an object. The result of such an event is that the finger bending is constrained, and the tracking error of the curvature controller increases. Once the error exceeds a predetermined threshold value, there is a switch from the reference tracking to the force controller, which maintains the finger in contact with the object. Therefore, the proposed architecture accounts not only for sensory data, but also for the error of the movement towards the touch. The proposed control architecture is illustrated with numerical simulations.

Keywords: Robotics, Estimation, Multivehicle systems
Abstract: Swarm applications use motion capture system or GPS sensors as localization systems. However, motion capture systems provide local solutions, and GPS sensors are not reliable in occluded environments. For reliable and versatile operation in a swarm, robots must sense each other and interact locally. Motivated by this requirement, we propose an onboard localization framework for multi-robot systems. Our framework consists of an anchor robot with three ultrawideband (UWB) sensors and a tag robot with a single UWB sensor. The anchor robot utilizes the three UWB sensors as a localization infrastructure and estimates the tag robot's location by using its on-board sensing and computational capabilities solely, without explicit inter-robot communication. We utilize a dual Monte-Carlo localization approach to capture the agile maneuvers of the tag robot with acceptable precision. We validate the effectiveness of our algorithm with simulations as well as indoor and outdoor experiments on a two-drone setup. The proposed framework with the dual MCL algorithm yields accurate estimates for various speed profiles of the tag robot, outperforms the standard particle filter and extended Kalman filter, and suffice for a relative position maintenance application.

Keywords: Robotics, Machine learning, Grey-box modeling
Abstract: This paper proposes a proprioceptive collision detection algorithm based on Gaussian Regression. Compared to sensor-based collision detection and other proprioceptive algorithms, the proposed approach has minimal sensing requirements, since only the currents and the joint configurations are needed. The algorithm extends the standard Gaussian Process models adopted in learning the robot inverse dynamics, using a more rich set of input locations and an ad-hoc kernel structure to model the complex and non-linear behaviors due to frictions in quasi-static configurations. Tests performed on a Universal Robots UR10 show the effectiveness of the proposed algorithm to detect when a collision has occurred.

Keywords: Robotics, Hybrid systems, Autonomous systems
Abstract: Tethered mobile robots have for a long time been utilized in search and rescue and operations in inaccessible or hazardous environments. From inspection and cleanup inside nuclear waste tanks to underwater inspection, tethers and umbilical cords have been a reliable means of uninterrupted power supply, high-bandwidth communication, and means of deployment and/or recovery in cases of system failure. While often tethered robots are teleoperated, this may not always be the case, and this paper is concerned with aspects of tether monitoring and management which are central to autonomous operations. Specifically, the paper reports on a method to approximate tether shape and configuration in cluttered workspaces for mobile robots equipped with spooling mechanisms capable of releasing or collecting a cable having its free end fixed at a point in the workspace.

Keywords: Robotics, Estimation, Optimization
Abstract: This paper presents a relative-constrained SLAM formulation where partial a priori landmark information is built into the SLAM problem. Incorporating a priori relative constraints is motivated by the desire to avoid drawbacks of global constraints and to reduce uncertainty in the overall map and pose estimates. First, a Relative Deterministic-Constrained SLAM (RDC-SLAM) method is presented, where a Lagrange multiplier term is added to the cost function of the standard graph-based SLAM method, realizing a new deterministic-constrained least squares solution. Next, this method is extended to incorporate probabilistic constraints and is solved using chance-constrained optimization for a more robust least square solution, leading to Relative Probabilistic-Constrained SLAM (RPC-SLAM). Both RDC-SLAM and RPC-SLAM are tested within a Monte-Carlo framework using a 2D dataset. It is shown that the RPC-SLAM framework outperforms the other methods considered when landmark initialization is poor.

Keywords: Stochastic optimal control, Sampled-data control, Autonomous systems
Abstract: We address the stochastic reach-avoid problem for linear systems with additive stochastic uncertainty. We seek to compute the maximum probability that the states remain in a safe set over a finite time horizon and reach a target set at the final time. We employ sampling-based methods and provide a lower bound on the number of scenarios required to guarantee that our estimate provides an underapproximation. Due to the probabilistic nature of the sampling-based methods, our underapproximation guarantee is probabilistic, and the proposed lower bound can be used to satisfy a prescribed probabilistic confidence level. To decrease the computational complexity, we propose a Voronoi partition-based to check the reach-avoid constraints at representative scenarios, instead of the original scenarios. The state constraints arising from the safe and target sets are tightened appropriately. We propose a systematic approach for selecting these representative scenarios and provide the flexibility to trade-off the number of cells needed for accuracy with the computational cost.

Keywords: Autonomous systems, Cooperative control, Control applications
Abstract: The purpose of this paper is to design a control law such that the multi-agent system can form into arbitrary shape, rotate around the centroid which tracks a given trajectory, and further adjust the formation into various shapes based on the affine transformation command. Moreover, the specified order between agents is crucial in some tasks, and hence ordered formation is addressed in our approach. The information for controller is measured locally from the neighbors and is in the local reference frames. To facilitate the goals, we propose an extended model and introduce the penalty flow exchanging mechanism which deals with the ordered formation. The control law is derived based on stability analysis, and a simulation example is provided to validate our results.

Keywords: Cooperative control, Autonomous systems, Autonomous robots
Abstract: We present a distributed control strategy for a team of agents to autonomously achieve a desired planar formation. Our control strategy is based on combining the barycentric coordinate-based (BCB) and the distance-based (DB) approach. In the BCB approach, the almost global convergence of the agents to the desired formation shape is guaranteed, however, the formation scale cannot be controlled. In the DB method, the scale of the achieved formation is controlled, however, the convergence is local and in general stable undesired equilibria exist. By combining these methods via imposing a timescale separation between their respective dynamics, our proposed control strategy retains the advantages of each approach and avoids their shortcomings. We analyze the stability properties of the proposed control and prove that the desired formation is an almost globally stable equilibrium. We provide simulations to typify the theoretical results and compare our method with a leader-follower BCB (LF-BCB) approach that can be used to control the formation scale in the BCB strategy. In particular, we demonstrate that unlike the LF-BCB approach, our method is far more robust to measurement inaccuracies.

Keywords: Autonomous systems, Multivehicle systems, Vision-based control
Abstract: This paper investigates the triangular and polygonal formation control problem for mobile multi-agent systems under the constraint that each agent can only take angle measurements. For triangular formations, due to the fact that the sum of three interior angles always equals pi, the desired triangular shape can be obtained when any two agents achieve desired angles for which they are the corresponding vertices of the triangle. So to achieve the desired shape of a triangular formation, we propose to let one agent remain fixed and the other two agents move along their bisectors respectively with respect to their two neighbors. For convex polygonal formations, since the sum of all interior angles is constant, we are able to use a similar control strategy to achieve the desired polygonal shape. The stability of the closed-loop multi-agent systems is proved using Lyapunov theory. Finally, simulation examples illustrate the validity of the theoretic results.

Keywords: Autonomous systems, Aerospace, Hybrid systems
Abstract: An uninhabited aerial vehicle (UAV) equipped with an electro-optical payload is tasked to collect over a set of discrete regions of interest. By considering the discrete regions to be obstacles that must be engaged, rather than avoided, a new mathematical technique emerges. To frame the anti-obstacle-avoidance problem, we use Kronecker indicator functions to localize the totality of constraints associated with the discrete regions. A rich class of payoff functionals can be defined using nonsmooth constructs. We show that the integrated sensor planning, scheduling and UAV maneuvering problem can be framed under a single unified mathematical framework. The price for this unification is nonsmooth calculus. The practical viability of the new problem formulation is demonstrated by solving a sample problem using DIDO — a guess-free, advanced MATLAB optimal control toolbox for solving dynamic optimization problems.

Keywords: Control system architecture, Autonomous systems, Flight control
Abstract: This paper discusses the development of a control architecture for hybrid Unmanned Aerial Vehicles (UAVs) based on model-free control (MFC) algorithms. Hybrid UAVs combine the beneficial features of fixed-wing UAVs with Vertical Take-Off and Landing (VTOL) capabilities to perform five different flight phases during typical missions, such as vertical take-off, transitioning flight, forward flight, hovering and vertical landing. Based on model-free control principles, a novel control architecture that handles the hybrid UAV dynamics at any flight phase is presented. This unified controller allows autonomous flights without discontinuities of switching for the entire flight envelope with position tracking, velocity control and attitude stabilization. Simulation results show that the proposed control architecture provides an effective control performance for the entire flight envelope and excellent disturbance rejections during the critical flight phases, such as transitioning and hovering flights in windy conditions.

Keywords: Distributed control, Decentralized control, Cooperative control
Abstract: For multi-agent systems, it is common to encode the task as an optimization problem with two distinctly different solution methodologies -- one is to directly apply control inputs as optimization updates, the other is to solve the optimization problem through communications before applying actual control inputs. This reveals an important trade-off between communication and execution overhead for control of multi-agent systems. To formally study this trade-off, we restrict our consideration to a class of commonly studied multi-agent problems where the objective function is the sum of a set of edge potential functions. The gradient descent algorithm and Newton's method are viewed as the proxy for the pure execution and the pure communication strategy, respectively. We propose an algorithm based on truncated Newton's method that provides tunable levels of trade-off between communication and execution efforts. Theoretical results on the convergence rate of the purposed algorithm are studied for the consensus problem under different trade-off strategies. The performance of the proposed algorithm is validated through simulation.

Keywords: Distributed control, Estimation, Cooperative control
Abstract: A simply structured distributed observer is described for estimating the state of a continuous-time, jointly observable, input-free linear system. The observer's correctness is established in a straightforward manner by exploiting several well-known properties of invariant subspaces.

Keywords: Distributed control, Agents-based systems
Abstract: In this paper, we propose a scalable distributed algorithm for double-layered multi-agent network to cooperatively find the least square solutions to an over-determined linear equation. Compared with existing consensus-based distributed linear equation solvers, the double-layered network structure allows us to implement two types of coordination, namely consensus and conservation, simultaneously. As a result, the proposed algorithm has achieved better scalability in the sense that each agent does not need to know a full row of the overall equation. The convergence of our algorithm is exponential, which has been validated by both analytical proof and numerical simulations.

Keywords: Distributed control, Fault detection, Networked control systems
Abstract: In this paper, a fully distributed averaging algorithm in the presence of adversarial Byzantine agents is proposed. The algorithm is based on a resilient retrieval procedure, where all non-Byzantine nodes send their own initial values and retrieve those of other agents. We establish that the convergence of the proposed algorithm relies on strong robustness of the graph, which is a connectivity notion. Simulation results are provided to verify the effectiveness of the proposed algorithms.

Keywords: Distributed control, Control of networks, Cooperative control
Abstract: This paper introduces a new definition of the linear cooperative output regulation problem in order to allow the common output synchronization (regulation) together with an additional output synchronization for a proper subset of all agents. The solvability of this problem with an internal model based distributed dynamic state feedback control law is first investigated based on a global condition. An agent-wise local sufficient condition is then presented under standard assumptions. A numerical example is finally provided to illustrate the considered problem and the proposed approach in this paper.

Keywords: Distributed control, Cooperative control, Control system architecture
Abstract: The focus of this paper is distributed control of multiagent systems in a-priori given, user-defined finite-time interval using a recently developed time transformation approach. In particular, we utilize a time transformation function to convert a user-defined finite-time interval to a stretched infinite-time interval such that a distributed control algorithm can be designed on this stretched interval and then it can be transformed back to the original finite-time interval in order to satisfy a given multiagent system objective. In addition, the robustness of the resulting finite-time distributed control algorithm against vanishing and non-vanishing system uncertainties is also discussed. In contrast to existing finite-time approaches, the presented algorithm can preserve a-priori given, user-defined finite-time convergence regardless of the initial conditions of the multiagent system and without the need for a knowledge of the upper bounds of the considered system uncertainty classes.

Keywords: Networked control systems, Control of networks, Game theory
Abstract: This paper studies the sensor placement problem in a networked control system for improving its security against cyber-physical attacks. The problem is formulated as a zero-sum game between an attacker and a detector. The attacker's decision is to select f nodes of the network to attack whereas the detector's decision is to place f sensors to detect the presence of the attack signals. In our formulation, the attacker minimizes its visibility, defined as the system L_2 gain from the attack signals to the deployed sensors' outputs, and the detector maximizes the visibility of the attack signals. The equilibrium strategy of the game determines the optimal locations of the sensors. The existence of Nash equilibrium for the attacker-detector game is studied when the underlying connectivity graph is a directed or an undirected tree. When the game does not admit a Nash equilibrium, it is shown that the Stackelberg equilibrium of the game, with the detector as the game leader, can be computed efficiently. Finally, the attacker-detector game is studied in a cooperative adaptive cruise control algorithm for vehicle platooning problem. The existence of Nash equilibrium is investigated for both directed and undirected platoons and the effect of the position of the reference vehicle on the game value is studied. Our results show that, under the optimal sensor placement strategy, an undirected topology provides a higher security level for a networked control system compared with its corresponding directed topology.

Keywords: Networked control systems, Autonomous robots, Distributed control
Abstract: We consider a scenario of cooperative task servicing, with a team of heterogeneous robots with different maximum speeds and communication radii, in charge of keeping the network intermittently connected. We abstract the task locations into a 1D cycle graph that is traversed by the communicating robots, and we discuss intermittent communication strategies so that each task location is periodically visited, with a worst–case revisiting time. Robots move forward and backward along the cycle graph, exchanging data with their previous and next neighbors when they meet, and updating their region boundaries. Asymptotically, each robot is in charge of a region of the cycle graph, depending on its capabilities. The method is distributed, and robots only exchange data when they meet.

Keywords: Networked control systems, Adaptive control, Observers for nonlinear systems
Abstract: This paper investigates adaptive neural network control for multiaxis trajectory tracking of a piezoelectric actuator-driven nanomanipulation system. An approximation model-based control scheme which is involved with only nominal parts of the unknown system dynamics is designed first and the hysteretic effect is compensated by designing a disturbance observer. Then, adaptive neural networks are applied to approximate unknown parts and an adaptive neural network control scheme is designed. It can be proved that all the error signals are ultimately bounded with Lyapunov’s stability theory. A nanomanipulation experiment is conducted and the results prove the effectiveness of the proposed control.

Keywords: Networked control systems, Control of networks, Network analysis and control
Abstract: A leader group selection approach that jointly considers network controllability and control energy is investigated in this work. Specifically, a dynamic multi-agent system with signed acyclic topology is considered, where signed edges capture cooperative and competitive interactions among agents. To effectively and efficiently control the multi-agent system, leader group selection in this work focuses on energy-related controllability, which jointly considers two primary objectives: 1) network controllability, i.e., identification of a subset of agents as leaders that can drive the entire network to a desired state even in the presence of antagonistic interactions, and 2) energy efficiency, which takes into account the control cost incurred by the selected leaders in steering the network to the desired state. To achieve these objectives, graph-inspired characterizations of energy-related controllability are developed based on the interaction between the network topology and the agent dynamics. The developed topological characterizations are exploited to derive heuristic leader selection algorithms on signed acyclic graphs. Illustrative examples are provided to demonstrate the effectiveness of the developed leader group selection methods.

Keywords: Networked control systems, Control system architecture, Supervisory control
Abstract: In this manuscript, we propose a novel control architecture capable of detecting deception attacks affecting networked control systems. In the proposed solution, we borrow and combine the existing concepts of watermarking signal and auxiliary systems in order to detect a broad class of false data injection attacks. We show that by combining the aforementioned concepts, we can detect false data injection attacks without sacrificing control performance. Moreover, we propose a novel nonlinear auxiliary system which is static and does not require any dynamical coupling with the plant dynamics. Finally, the effectiveness of the proposed method is shown by means of a simulation campaign.

Keywords: Networked control systems, Decentralized control, Hybrid systems
Abstract: Decentralized periodic event-triggered control (DPETC) strategies are an attractive solution for wireless cyber-physical systems where resources such as network bandwidth and sensor power are scarce. This is because these strategies have the advantage of preventing unnecessary data transmissions and therefore reduce bandwidth and energy requirements, however the sensor sampling regime remains synchronous. Typically the action of sampling leads almost immediately to a transmission on an event being detected. If the sampling is synchronous, multiple transmission requests may be raised at the same time which further leads to bursty traffic patterns. Bursty traffic patterns are critical to the DPETC systems performance as the probability of collisions and the amount of requested bandwidth resources become high ultimately causing delays. In this paper, we propose an asynchronous sampling scheme for DPETC. The scheme ensures that at each sampling time, no more than one transmission request can be generated which prevents the occurrence of network traffic collision. At the same time, for the DPETC system with asynchronous sampling a pre-designed global exponential stability and L2-gain performance can still be guaranteed. We illustrate the effectiveness of the approach through a numerical example.

Keywords: Optimization, Hybrid systems, Switched systems
Abstract: This paper proposes hybrid control algorithms for optimization of a convex objective function with fast convergence, reduced oscillations, and robustness. Developed using hybrid system tools, the algorithms feature a uniting control strategy, in which two standard heavy ball algorithms, one used globally and another used locally, with properly designed gravity and friction parameters, are employed. The proposed hybrid control strategy switches the parameters to converge quickly to the set of minimizers of the convex objective function without oscillations. A hybrid control algorithm implementing a switching strategy that measures the objective function and its gradient, and another algorithm that only measures its gradient, are designed. Key properties of the resulting closed-loop systems, including existence of solutions, asymptotic stability, and robustness, are analyzed. Numerical results validate the findings.

Keywords: Optimization, Embedded systems, Automotive control
Abstract: Real-time applications in today’s distributed cyber-physical control systems are often represented as Precedence-constrained Task Graphs (PTGs) and increasingly implemented on heterogeneous platforms, to cater to their high performance demands. Optimal scheduling solutions for such systems can provide advantages in terms of performance, reliability, cost etc. This paper addresses the problem of scheduling a real-time application modelled as PTG where tasks have multiple optional service levels (where higher service level implies higher Quality-of-Service (QoS)). In particular, we propose an Integer Linear Programming based optimal solution strategy for scheduling PTGs with multiple service levels, executing on a distributed platform composed of heterogeneous processing elements. Through the real-world case study of an automotive cruise controller, we generate an optimal schedule using our proposed scheme in order to demonstrate its applicability. Conducted simulation based experiments and comparison with a state of art approach, reveal the practical efficacy of our scheme.

Keywords: Optimization, Control applications
Abstract: In this paper, we propose a dynamical systems perspective of the Expectation-Maximization (EM) algorithm. More precisely, we can analyze the EM algorithm as a nonlinear state-space dynamical system. The EM algorithm is widely adopted for data clustering and density estimation in statistics, control systems, and machine learning. This algorithm belongs to a large class of iterative algorithms known as proximal point methods. In particular, we re-interpret limit points of the EM algorithm and other local maximizers of the likelihood function it seeks to optimize as equilibria in its dynamical system representation. Furthermore, we propose to assess its convergence as asymptotic stability in the sense of Lyapunov. As a consequence, we proceed by leveraging recent results regarding discrete-time Lyapunov stability theory in order to establish asymptotic stability (and thus, convergence) in the dynamical system representation of the EM algorithm.

Keywords: Optimization, Game theory, Computational methods
Abstract: Motivated by multi-user optimization problems and non-cooperative Nash games in stochastic regimes, we consider stochastic variational inequality (SVI) problems on matrix spaces where the variables are positive semidefinite matrices and the mapping is merely monotone. Much of the interest in the theory of variational inequality (VI) has focused on addressing VIs on vector spaces. Yet, most existing methods addressing VIs on matrix spaces either rely on strong assumptions, or require a two-loop framework where at each iteration, a projection problem, i.e., a semidefinite optimization problem needs to be solved. Motivated by this gap, we develop a stochastic mirror descent method where we choose the distance generating function to be defined as the quantum entropy. This method is a single-loop first-order method in the sense that it only requires a gradient-type of update at each iteration. The novelty of this work lies in the convergence analysis that is carried out through employing an auxiliary sequence of stochastic matrices. Our contribution is three-fold: (i) under this setting and employing averaging techniques, we show that the iterate generated by the algorithm converges to a weak solution of the SVI; (ii) moreover, we derive a convergence rate in terms of the expected value of a suitably defined gap function; (iii) we implement the developed method for solving a multiple-input multiple-output multi-cell cellular wireless network composed of seven hexagonal cells and present the numerical experiments supporting the convergence of the proposed method.

Keywords: Optimization, Flexible structures, Aerospace
Abstract: A novel input and output blend calculation method is presented for decoupled control of selected modes. Decoupling is carried out by maximizing the minimum sensitivity of the controlled mode while minimizing the worst case gain for other mode(s) from the blended input to the blended output. This leads to an optimization problem of joint maximization of the mathcal{H}_{-} index of the controlled mode and the minimization of the mathcal{H}_{infty} norm corresponding to other modes. The optimization problem is formalized with Linear Matrix Inequalities, and two examples from the aerospace engineering field are given for evaluation purposes.

Keywords: Optimization, Kalman filtering, Reduced order modeling
Abstract: Battery aging remains a critical challenge and state-of-heath (SOH) estimation is a key task for battery management system (BMS). Adaptive model-based methods have been extensively studied, and conventional applications of extended Kalman filter (EKF) are mostly limited to only one electrode, in order to address the weak observability of battery system due to the absence of a reference electrode. Moreover, the state-of-art is dominated by improving model precision and algorithm efficiency, while the significance of measurement data for SOH estimation is often neglected. This study applies EKF to both electrodes and guarantees the estimation accuracy through the optimized initialization. Effective cyclable lithium, ∆n_(Li,avg), is proposed and validated with experimental data as a reliable aging parameter to interpret the long-term evolution of battery degradation. Data of different operating conditions (charging, discharging, charge depleting), different state-of-charge (SOC) sections and different rates of measurement update, are tested for their impacts on aging estimation. It can be concluded that a sufficiently wide SOC range from 1C charging is comparatively the optimal condition for estimating ∆n_(Li,avg). Within the bond of required estimation precision, a slower measurement update proves to be a preferred solution due to its equivalent estimation accuracy and less demanding computational resource.

Keywords: Neural networks
Abstract: In this paper, we analyze the spatio-temporal mean field model developed by Liley et al. [1] in order to advance our understanding of the wide effects of pharmacological agents and anesthetics. Specifically, we use the spatio-temporal mean field model in [1] for capturing the electrical activity in the neocortex to computationally study the emergence of α and γ-band rhythmic activity in the brain. We show that α oscillations in the solutions of the model appear globally across the neocortex, whereas γ oscillations can emerge locally as a result of a bifurcation in the dynamics of the model. We solve the dynamic equations of the model using a finite element solver package and show that our results verify the predictions made by bifurcation analysis.

Keywords: Biological systems, Network analysis and control, Neural networks
Abstract: Oscillations in the brain are one of the most ubiquitous and robust patterns of activity and correlate with various cognitive phenomena. In this work, we study the existence and properties of oscillations in simple mean-field models of brain activity with bounded linear-threshold rate dynamics. First, we obtain exact conditions for the existence of limit cycles in two-dimensional excitatory-inhibitory networks (E-I pairs). Building on this result, we study networks of multiple E-I pairs, provide exact conditions for the lack of stable equilibria, and numerically show that this is a tight proxy for the existence of oscillatory behavior. Finally, we study cross-frequency coupling between pairs of oscillators each consisting of an E-I pair. We find that while both phase-phase coupling (synchronization) and phase-amplitude coupling (PAC) monotonically increase with inter-oscillator connection strength, there exists a tradeoff in increasing frequency mismatch between the oscillators as it de-synchronizes them while enhancing their PAC.

Keywords: Human-in-the-loop control, Markov processes, Network analysis and control
Abstract: We consider an extension of the Rescorla-Wagner model which bridges the gap between conditioning and learning on a neural-cognitive, individual psychological level, and the social population level. In this model, the interaction among individuals is captured by a Markov process. The resulting human-social behavior model is a recurrent iterated function system which behaves differently from the classical Rescorla-Wagner model due to randomness. A sufficient condition for the convergence of the forward process starting with arbitrary initial distribution is provided. Furthermore, the ergodicity properties of the internal states of agents in the proposed model are studied.

Keywords: Stability of nonlinear systems, Network analysis and control, Neural networks
Abstract: In this paper we derive exact and approximate conditions for the (local) stability of the cluster synchronization manifold for sparsely interconnected oscillators with heterogeneous and weighted Kuramoto dynamics. Cluster synchronization, which emerges when the oscillators can be partitioned in a way that their phases remain identical over time within each group, is critically important for normal and abnormal behaviors in technological and biological systems ranging from the power grid to the human brain. Yet, despite its importance, cluster synchronization has received limited attention, so that the fundamental mechanisms regulating cluster synchronization in important classes of oscillatory networks are still unknown. In this paper we provide the first conditions for the stability of the cluster synchronization manifold for general weighted networks of heterogeneous oscillators with Kuramoto dynamics. In particular, we discuss how existing results are inapplicable or insufficient to characterize the stability of cluster synchronization for oscillators with Kuramoto dynamics, provide rigorous quantitative conditions that reveal how the network weights and oscillators' natural frequencies regulate cluster synchronization, and offer examples to quantify the tightness of our conditions. Further, we develop approximate conditions that, despite their heuristic nature, are numerically shown to tightly capture the transition to stability of the cluster synchronization manifold.

Keywords: Neural networks, Learning, Biomedical
Abstract: Controlling a population of interconnected neurons using extrinsic stimulation is a challenging problem. The challenges are due to the inherent nonlinear neuronal dynamics, the highly complex structure of underlying neuronal networks, the underactuated nature of the control problem, and adding to these is the binary nature of the observation/feedback. To meet these challenges, adaptive, learning-based approaches using deep neural networks and reinforcement learning are potentially useful strategies. In this paper, we propose an approximation based learning framework in which a model for approximating the input-output relationship in a spiking neuron is developed. We then present a reinforcement learning scheme to approximate the solution for the Bellman equation, and to design the control sequence to achieve a desired spike pattern. The proposed strategy, by integrating the reinforcement learning and system theoretic approaches, provides a tractable framework to design a learning control network, and to select the hyper parameters in deep learning architectures. We demonstrate the feasibility of the proposed approach using numerical simulations.

Keywords: Estimation, Biological systems, Optimization algorithms
Abstract: Despite significant effort in understanding complex systems (CS), we lack a theory for modeling, inference, analysis and efficient control of time-varying complex networks (TVCNs) in uncertain environments. From brain activity dynamics to microbiome, and even chromatin interactions within the genome architecture, many such TVCNs exhibits a pronounced spatio-temporal fractality. Moreover, for many TVCNs only limited information (e.g., few variables) is accessible for modeling, which hampers the capabilities of analytical tools to uncover the true degrees of freedom and infer the CS model, the hidden states and their parameters. Another fundamental limitation is that of understanding and unveiling of unknown drivers of the dynamics that could sporadically excite the network in ways that straightforward modeling does not work due to our inability to model non-stationary processes. Towards addressing these challenges, in this paper, we consider the problem of learning the fractional dynamical complex networks under unknown unknowns (i.e., hidden drivers) and partial observability (i.e., only partial data is available). More precisely, we consider a generalized modeling approach of TVCNs consisting of discrete-time fractional dynamical equations and propose an iterative framework to determine the network parameterization and predict the state of the system. We showcase the performance of the proposed framework in the context of task classification using real electroencephalogram data.

Keywords: Building and facility automation, Process Control, Optimization
Abstract: Self-optimizing Control (SOC) is a method for finding appropriate controlled variables for which implementation of feedback control yields nearly-optimal operation regardless of variation in disturbances. The Jacobian estimation process in conventional SOC rely on an offline analysis of large amounts of steady-state data, which can be difficult in practice. In this paper, we propose a new SOC procedure enabled by extremum-seeking control (ESC). First, by presenting periodic disturbance dither into the plant model, the Jacobian estimation can be carried out with the dither-demodulation process in multivariable ESC, and then the null-space method is used to find the optimal sensitivity matrix. The ESC can then be used to find the optimum setpoint value for the controlled variable from the previous step. The proposed method is compared with conventional SOC using a Modelica-based dynamic simulation of an air-source heat pump (ASHP) system.

Keywords: Optimal control, Energy systems, Building and facility automation
Abstract: In building systems, it is necessary to maintain comfort conditions while minimizing operating costs. One way to do this is to use a real-time optimizer (RTO) to adjust the setpoints of the controlled variables. Another way is to use a self-optimizing control (SOC) structure to find one or more new variables that, when controlled to the appropriate constant setpoints, drive the operating cost of the system to, or close to, its optimal point. A requirement of SOC is that an optimal operating point be used in the calculation of the self-optimizing variable(s) and the identification of its parameters. In this work, we propose a formulation of SOC that allows for the use of non-optimal data. The proposed method makes use of normal operating data to identify the parameters used to calculate the self-optimizing variables. Simulation of an HVAC system shows that the performance of the proposed method is similar to that of SOC based on optimal data, and also to the performance of an RTO alternative based on extremum-seeking control (ESC).

Keywords: Energy systems, Optimal control, Mechatronics
Abstract: This paper presents a new control method to minimize the energy consumption of a micro-scale concentrated solar power (MicroCSP) system and building heating, ventilation, and air conditioning (HVAC) system. A new real-time optimal control method is proposed using the concept of “exergy” and model predictive control (MPC) techniques. To achieve this, first law of thermodynamics (FLT) and second law of thermodynamics (SLT) based mathematical models of MicroCSP are developed and integrated into a model of an office building located at Michigan Technological University. Then, an exergy-wise MPC framework is designed to optimize MicroCSP operation in accordance with the building HVAC needs. The new controller reduces exergy destruction by 28%, compared to a common rule-based controller (RBC). This leads to 23% energy saving, compared to the applied RBC.

Keywords: Building and facility automation, Fuzzy systems, Optimization
Abstract: This paper presents the design of an easily implementable rule-based controller that can minimize the electrical energy consumption of a building heating, ventilation, and air-conditioning (HVAC) system integrated with a micro-scale concentrated solar power (MicroCSP) system. A model predictive control (MPC) scheme is developed to optimize MicroCSP electrical and thermal energy flows for HVAC use in a building. Despite its attractiveness regarding energy savings and thermal comfort satisfaction, MPC requires high computational resources and can not be easily implemented on the common low-cost HVAC controllers available in the market. To cope with these issues, two MPC-trained adaptive neuro-fuzzy inference system (ANFIS) models are designed to control the building HVAC with MicroCSP. Simulation results exploiting real operation data from an office building at Michigan Technological University and our newly purchased MicroCSP are presented. It is shown that the resulting controller can reproduce the MPC reasoning and performance while being simpler and much more computationally efficient.

Keywords: Building and facility automation, Game theory, Human-in-the-loop control
Abstract: Integrating the preferences of building occupants for efficient building energy management has the potential for significant energy savings. In this paper, we present a new dynamic mechanism that achieves ex-post incentive compatibility, i.e., the occupants reveal their privately-held preferences truthfully in every time period. These preferences are then incorporated in a receding horizon control scheme to jointly minimize the cost of energy purchase and the discomfort experienced by the occupants. We evaluate the performance of our scheme with a baseline heating policy based on standardized thermal comfort bounds, and the classical dynamic pivot mechanism via extensive numerical simulations of a sample building. We illustrate that due to the integration of individual preferences, both occupant discomfort and energy consumption can be greatly reduced compared to the baseline heating policy. Furthermore, while the dynamic pivot mechanism requires occupants to pay for their comfort, our payment scheme rewards the occupants whose participation leads to energy savings, making their participation in the scheme more palatable.

Keywords: Building and facility automation, Energy systems, Predictive control for nonlinear systems
Abstract: Although Model Predictive Control (MPC) has been widely investigated for energy efficient climate control of buildings, most prior works have neglected humidity in the problem formulation and performance evaluations. A climate control algorithm that ignores humidity cannot be used in practice, especially in hot-humid climates. Apart from the discomfort of occupants, high humidity over long periods will lead to issues such as mold growth, adversely impacting occupant health. In this paper, we provide an MPC formulation that explicitly accounts for humidity constraints in a principled manner. We show how to construct data-driven low order models of a cooling and dehumidifying coil that can be used in the MPC formulation. The resulting controller's performance is tested in simulation using a plant that differs significantly from the model used by the optimizer. In spite of the large plant model mismatch, the proposed MPC controller performs well. Humidity constraints are seen to be active for a large part of the day, especially in the summer. MPC formulations that ignore humidity would lead to a poor indoor climate in these situations.

Keywords: Learning, Agents-based systems, Fault tolerant systems
Abstract: We study a setting where a group of agents, each receiving partially informative private observations, seek to collaboratively learn the true state (among a set of hypotheses) that explains their joint observation profiles over time. To solve this problem, we propose a distributed learning rule that differs fundamentally from existing approaches, in the sense that it does not employ any form of "belief-averaging". Specifically, every agent maintains a local belief (on each hypothesis) that is updated in a Bayesian manner without any network influence, and an actual belief that is updated (up to normalization) as the minimum of its own local belief and the actual beliefs of its neighbors. Under minimal requirements on the signal structures of the agents and the underlying communication graph, we establish consistency of the proposed belief update rule, i.e., we show that the actual beliefs of the agents asymptotically concentrate on the true state almost surely. As one of the key benefits of our approach, we show that our learning rule can be extended to scenarios that capture misbehavior on the part of certain agents in the network, modeled via the Byzantine adversary model. In particular, we prove that each non-adversarial agent can asymptotically learn the true state of the world almost surely, under appropriate conditions on the observation model and the network topology.

Keywords: Learning, Autonomous robots, Numerical algorithms
Abstract: In problems such as surveying or monitoring remote regions, a mobile robot must transmit data over a wireless network with unknown, position-dependent transmission rates. We propose an algorithm to achieve this objective that learns approximations of the rate function and of an optimal-control solution that transmits the data in minimum time. The rates are estimated with supervised learning from the samples observed; and the control is found with dynamic programming sweeps around the current state of the robot that exploit the rate function estimate, combined with online reinforcement learning. For both synthetic and realistic rate functions, our experiments show that the learning algorithm empties the data buffer in less than twice the number of steps achieved by a model-based solution that requires to perfectly know the rate function.

Keywords: Learning, Autonomous systems, Optimization
Abstract: This paper presents an online, robust, and model-free motion planning framework for kinodynamic systems. In particular, we employ a Q-learning algorithm for a two player zero-sum dynamic game to account for worst-case disturbances and kinodynamic constraints. We use one critic, and two actor approximators to solve online the finite horizon minimax problem with a form of integral reinforcement learning. We then leverage a terminal state evaluation structure to facilitate the online implementation. A static obstacle augmentation, and a local re-planning framework is presented to guarantee safe kinodynamic motion planning. Rigorous Lyapunov-based proofs are provided to guarantee closed-loop stability, while maintaining robustness and optimality. We finally evaluate the efficacy of the proposed framework with simulations and we provide a qualitative comparison of kinodynamic motion planning techniques.

Keywords: Learning, Control applications, Autonomous robots
Abstract: In this work, we propose a novel, learning-based approach for swift maneuver planning of unmanned aerial vehicles using motion primitives. Our approach is composed of two main stages: learning a set of motion primitives during offline training first, and utilization of them for online planning of fast maneuvers thereafter. We propose a compact disposition of motion primitives which consists of roll, pitch, and yaw motions to build up a simple yet effective representation for learning. Thanks to this compact representation, our method retains an easily transferable, reproducible, and referable knowledge which caters for real-time swift maneuver planning. We compare our approach with the current state-of-the-art methods for planning and control, and show improved navigation time performance up to 25% in challenging obstacle courses. We validate our approach through software-in-the-loop Gazebo simulations and real flight tests with Diatone FPV250 Quadcopter equipped with PX4 FMU.

Keywords: Learning, Formal verification/synthesis, Autonomous robots
Abstract: Signal temporal logic (STL) provides a user-friendly interface for defining complex tasks for robotic systems. Recent efforts aim at designing control laws or using reinforcement learning methods to find policies which guarantee satisfaction of these tasks. While the former suffer from the trade-off between task specification and computational complexity, the latter encounter difficulties in exploration as the tasks become more complex and challenging to satisfy. This paper proposes to combine the benefits of the two approaches and use an efficient prescribed performance control (PPC) base law to guide exploration within the reinforcement learning algorithm. The potential of the method is demonstrated in a simulated environment through two sample navigational tasks.

Keywords: Learning, Iterative learning control
Abstract: Projective cone scheduling defines a large class of rate-stabilizing policies for queueing models relevant to several applications. While there exists considerable theory on the properties of projective cone schedulers, there is little practical guidance on choosing the parameters that define them. In this paper, we propose an algorithm for designing an automated projective cone scheduling system based on observations of an expert projective cone scheduler. We show that the estimated scheduling policy is able to emulate the expert in the sense that the average loss realized by the learned policy will converge to zero. Specifically, for a system with n queues observed over a time horizon T, the average loss for the algorithm is O(ln(T)sqrt{ln(n)/T}). This upper bound holds regardless of the statistical characteristics of the system. The algorithm uses the multiplicative weights update method and can be applied online so that additional observations of the expert scheduler can be used to improve an existing estimate of the policy. This provides a data-driven method for designing a scheduling policy based on observations of a human expert. We demonstrate the efficacy of the algorithm with a simple numerical example and discuss several extensions.

Keywords: Automotive systems, Mechatronics, Predictive control for nonlinear systems
Abstract: The electric turbocharger provides great potential for vehicle fuel efficiency improvement, exhaust emissions reduction and transient response acceleration. It makes the engine runs as a hybrid system so critical challenges are raised in energy management and control. This paper proposes a real-time energy management strategy for the electric turbocharger. A multi-variable explicit model predictive controller is designed to regulate the key variables in the engine air system, while the optimal setpoints of those variables are generated by a high level controller. The controllers work in a highly efficient way to achieve the optimal energy management. This strategy has been validated in simulations and experiments. Excellent tracking performance and high robustness demonstrate the effectiveness of the proposed method.

Keywords: Automotive control, Optimal control, Automotive systems
Abstract: Recent trends in automotive industry reveal strict movement towards electromobility. However, limited electric vehicle (EV) range poses a major impediment of this movement which is tackled by development of sophisticated advanced driver assistance systems (ADAS). This paper presents a novel approach for computation of energy efficient speed trajectories for EVs. The proposed approach aims for application in an intelligent cruise controller (ICC) framework utilizing lookahead data for the road provided by advanced navigational systems and a simplified vehicle model. To evaluate the algorithm's performance, a simulative study is carried out comparing optimized speed trajectories to baseline ICC. Distinctive feature of this work presents the derivation of a simplified powertrain model which is used in linear MPC. This makes the proposed approach perfectly suitable for prototypical implementation and offers potential for transfer to standard electronic control units.

Keywords: Automotive control, Output regulation, Algebraic/geometric methods
Abstract: Electric boosting is a promising solution to improve the dynamic response of the air path system in downsized turbocharged engines. Unfortunately, typical electric supercharge controllers do not take into account the action of the wastegate actuator when generating the references for the e-compressor shaft speed, which can lead to overshoots and poor overall tracking performance. Leveraging on the presence of an existing feedback tracking controller for the wastegate and e-compressor, a plug-in module based on the inverse model allocation framework is design to coordinate the action of the two actuators. The module is tested using a model in the loop platform, based on a high-fidelity engine model in GT-Suite. Significant reduction of power losses are observed for a launch maneuver.

Keywords: Automotive control, Mechanical systems/robotics, Mechatronics
Abstract: In this paper, a new method for vehicle torque vectoring (TV) with the difference in torques between the left and right sides is introduced. This chassis control system can help the vehicle follow the driver’s intended line without vehicle deceleration. Targeting a hybrid four wheel drive (4WD) vehicle with an active differential in rear axle and in-wheel motors (IWMs) in front axle, a TV system based on a vehicle bicycle model is developed. Especially, a new yaw rate reference smoothly varying between safety mode and sport mode aims to enhance both cornering safety and agility. To properly combine these two modes, a weighting factor based on steering command is designed. Also, to track the reference accurately, an integral sliding mode controller (SMC) is introduced in this paper, which has the advantage of reducing yaw rate error during steady state cornering. Lastly, a torque distribution method based on modeling of both active differential and IWM is developed to generate the correct yaw moment. Also, it reflects the actuator characteristics, such as response time, torque capacity, and torque direction. The performance of the proposed TV system is evaluated using a vehicle dynamic software, and a comparative study is also conducted.

Keywords: Automotive systems
Abstract: This paper proposes an integrated energy and thermal management system (IETMS) to quantify the influence of a cold-start on the fuel-saving potential and the gain of utilizing waste heat recovery (WHR) technologies on the ultimate fuel saving of a plug-in hybrid electric vehicle with cabin heating. A cold-start indicates a low engine temperature, which increases the frictional power loss, resulting in excess fuel consumption. A dual source WHR (DSWHR) system harvests waste heat from exhaust gases and the recovered power is temporarily stored into the battery and can be retrieved when needed. Furthermore, it recuperates waste energy from a continuously variable transmission and an electric machine including power electronics to increase the heating performance of a heat pump, which reduces the load on the battery. For a known driving cycle, New European Driving Cycle, the IETMS aims to maximize the fuel efficiency. Numerical results demonstrate that a cold-start has a remarkable impact on the fuel-saving potential, 7.1%, yet a small influence on the optimization strategy. The DSWHR system shows a significant improvement on the ultimate fuel saving, up to 13.1%, from which insights into the design of WHR technologies can be drawn.

Keywords: Automotive systems, Modeling, Automotive control
Abstract: The net force generated by tire moments on the steering rack of a vehicle, called the rack force, holds a significant amount of information about the tire-road angle, the surface profile of road, and the speed of the vehicle. As a result, many electric power steering control applications rely on the estimation of rack force. Current methods of rack force estimation are inaccurate because they either do not account for road profile in the rack force estimation or are susceptible to disturbances acting in the steering system. In this paper we overcome both of these limitations and develop two real-time capable models to estimate the rack force for driving on longitudinal and lateral road slopes using vehicle and tire dynamics models. We demonstrate the accuracy of our models by analyzing the results from actual driving experiments performed on sloped roads under normal and aggressive driving situations. We also compare the performance of the two models with a model existing in the literature that does not account for road slopes.

Keywords: Predictive control for linear systems, Constrained control, Multivehicle systems
Abstract: This paper addresses the problem of collision avoidance in a multi-obstacle environment and focuses on its representation in optimization-based control problems. The design problem is commonly stated in the literature in terms of a constrained optimization problem over a non-convex domain. Preliminary results make use of hyperplane arrangements to characterize these regions. The current paper considers additional structural constraints by the use of zonotopic over-approximation and highlights their benefits when introduced in the obstacle avoidance problem. Comparisons with classical sampled-based approaches are presented through simulations.

Keywords: Predictive control for linear systems, Indirect adaptive control, Delay systems
Abstract: In this paper, a robust predictive control with plant parameter adaptation for time-delay systems is designed. The controller is divided into two parts in order to tackle two problems, the uncertainty adaptation, and the output tracking control. The first control part is used to compensate an unknown parameter vector while the other predictive part deals with the output tracking control. Theoretical results are implemented for two cases of study, the first one thanks to simulations for a third order system, and the second one on real-time application to a circuit system.

Keywords: Predictive control for linear systems, Large-scale systems, Optimal control
Abstract: We propose a model predictive scheme for selecting actuators in dynamical systems. In control applications, selection problems arise due to the high cost associated to simultaneously using all sensors or actuators in large-scale systems. Since these problems are NP-hard in general, finding an optimal solutions is impractical and approximations based on greedy or convex relaxations are commonly used. In most approaches, however, the control policy and actuator subsets are obtained a priori. In this work, we address the online problem using a model predictive selection (MPS). This iterative procedure inspired by model predictive control methods determines a near-optimal actuator subset for a finite operation horizon starting at the current state, applies the first control action on this subset, and repeats the procedure starting from the new state. Despite using suboptimal solutions of the selection problem, we derive conditions that guarantee this procedure is stable. We illustrate these conditions for the LQR problem by leveraging the concept of approximate submodularity and conclude with numerical experiments that showcase the use of the proposed approach.

Keywords: Predictive control for linear systems, Neural networks, Machine learning
Abstract: In this paper, we propose a novel framework for approximating the explicit MPC law for linear parameter-varying systems using supervised learning. In contrast to most existing approaches, we not only learn the control policy, but also a "certificate policy", that allows us to estimate the suboptimality of the learned control policy online, during execution-time. We learn both these policies from data using supervised learning techniques, and also provide a randomized method that allows us to guarantee the quality of each learned policy, measured in terms of feasibility and optimality. This in turn allows us to bound the probability of the learned control policy of being infeasible or suboptimal, where the check is performed by the certificate policy. Since our algorithm does not require the solution of an optimization problem during run- time, it can be deployed even on resource-constrained systems. We illustrate the efficacy of the proposed framework on a vehicle dynamics control problem where we demonstrate a speedup of up to two orders of magnitude compared to online optimization with minimal performance degradation.

Keywords: Predictive control for linear systems, Uncertain systems, Stochastic systems
Abstract: Stochastic model predictive control (SMPC) formulations are proposed that have both low on-line computational cost and zero steady-state offset for constrained dynamical systems of high state dimension. The effects of probabilistic parameter uncertainties on the process outputs are quantified using polynomial chaos theory, and the scalability with state dimension is obtained by using an input-output formulation. An explanation is provided for why the structure of some SMPC formulations provide zero steady-state error whereas other seemingly reasonable formulations do not. The article also describes how to incorporate chance constraints on the states and outputs into the SMPC formulations, while retaining an online optimization whose cost has a weak dependency on the number of states and outputs.

Keywords: Predictive control for linear systems
Abstract: This paper presents two significant contributions to the understanding of Predictive Functional Control (PFC). First, it gives novel insights and explanations into a poorly understood issue, that is the weak link between PFC tuning parameters and the resulting closed-loop behaviour. This new understanding is then exploited to proposed a modification to the existing PFC algorithm which creates a much stronger tuning link while retaining the critical properties of elementary coding and understanding. The efficacy of the proposal is demonstrated on several numerical examples.

Keywords: Randomized algorithms, Automata, Formal verification/synthesis
Abstract: In this paper, we develop a privacy implementation for symbolic control systems. Such systems generate sequences of non-numerical data, and these sequences can be represented by words or strings over a finite alphabet. This work uses the framework of differential privacy, which is a statistical notion of privacy that makes it unlikely that privatized data will reveal anything meaningful about underlying sensitive data. To bring differential privacy to symbolic control systems, we develop an exponential mechanism that approximates a sensitive word using a randomly chosen word that is likely to be near it. The notion of “near” is given by the Levenshtein distance, which counts the number of operations required to change one string into another. We then develop a Levenshtein automaton implementation of our exponential mechanism that efficiently generates privatized output words. This automaton has letters as its states, and this work develops transition probabilities among these states that give overall output words obeying the distribution required by the exponential mechanism. Numerical results are provided to demonstrate this technique for both strings of English words and runs of a deterministic transition system, demonstrating in both cases that privacy can be provided in this setting while maintaining a reasonable degree of accuracy.

Keywords: Networked control systems, Quantized systems, Stability of linear systems
Abstract: We study communication-constrained networked control problems for linear time-invariant systems in the presence of Denial-of-Service (DoS) attacks, namely attacks that prevent transmissions over the communication network. Our work aims at exploring the relationship between system resilience and network bandwidth. Given a class of DoS attacks, we characterize the bit-rate bounds that are dependent on the unstable eigenvalues of the dynamic matrix of the plant and the parameters of DoS attacks, beyond which exponential stability of the closed-loop system can be guaranteed. Our characterization clearly shows the trade-off between the communication bandwidth and resilience against DoS. An example is given to illustrate the proposed solution approach.

Keywords: Fault detection, Linear systems, Output regulation
Abstract: This article proposes a new approach for replay attack detection using a model inversion based physical watermark. We consider a defender who tracks a constant reference at the output. This leaves a system vulnerable to replay attacks, where an adversary replaces the true outputs of a system with a recorded sequence. In steady state a defender can not distinguish between normal and replayed outputs, allowing an undetected attack. This paper argues that a controller using model inversion can achieve simultaneous tracking and security performance. Specifically, this approach computes a feedforward input using a technique called pseudo-inversion, which is then added to a constant reference signal. Beyond considering physical watermarking in a new setting of output tracking, the main advantage of this approach is the performance guarantees of the associated controller. Unlike classical physical watermarking which introduces stochastic inputs that can lead to potentially undesirable behavior, model inversion watermarking allows a defender to carefully utilize several degrees of freedom to achieve predictable control performance during normal operation and detect malicious behavior while under replay attack. In this paper, we focus on a practical scheme, which uses time resets. Moreover, we demonstrate our solution can be applied to nonminimum phase systems.

Keywords: Networked control systems, Switched systems, Estimation
Abstract: Cyber-Physical Systems (CPS) have traditionally been considered more static, with regular communication patterns when compared to classical information technology networks. Because the structure of most CPS remains unchanged during long periods of time, they become vulnerable to adversaries who can tailor their attacks based on their precise knowledge of the system dynamics, communications, and control. Moving Target Defense (MTD) has emerged as a strategy to add uncertainty about the state and execution of a system in order to prevent adversaries from having predictable effects with their attacks. In this work we propose a novel type of MTD strategy that randomly changes the availability of the sensor data, so that it is harder for adversaries to tailor stealthy attacks and at the same time it can minimize the impact of false-data injection attacks. Using tools from switched control systems we formulate an optimization problem to find the probability of the switching signals that increase the visibility of stealthy attacks while decreasing the deviation caused by false data injection attacks.

Keywords: Networked control systems, Emerging control applications
Abstract: A malicious attacker with access to the sensor channel in a feedback control system can severely affect the physical system under control, while simultaneously being hard to detect. A properly designed anomaly detector can restrict the impact of such attacks, however. Anomaly detectors with an internal state (stateful detectors) have gained popularity because they seem to be able to mitigate these attacks more than detectors without a state (stateless detectors). In the analysis of attacks against control systems with anomaly detectors, it has been assumed that the attacker has access to the detector's internal state, or designs its attack such that it is not detected regardless of the detector's state. In this paper, we show how an attacker can realize the first case by breaking the confidentiality of a stateful detector state evolving with linear dynamics, while remaining undetected and imitating the statistics of the detector under nominal conditions. The realization of the attack is posed in a convex optimization framework using the notion of Kullback-Leibler divergence. Further, the attack is designed such that the maximum mean estimation error of the Kalman filter is maximized at each time step by exploiting dual norms. A numerical example is given to illustrate the results.

Keywords: Fault detection, Stochastic systems, Control applications
Abstract: Previously, we derived exact relationships between the properties of a linear time-invariant control system and properties of an anomaly detector that quantified the impact an attacker can have on the system if that attacker aims to remain stealthy to the detector. A necessary first step in this process is to be able to precisely tune the detector to a desired level of performance (false alarm rate) under normal operation, typically through the selection of a threshold parameter. To-date efforts have only considered Gaussian noises. Here we generalize the approach to tune a chi-squared anomaly detector for noises with non-Gaussian distributions. Our method leverages a Gaussian Mixture Model to represent the arbitrary noise distributions, which preserves analytic tractability and provides an informative interpretation in terms of a collection of chi-squared detectors and multiple Gaussian disturbances.

Keywords: Adaptive control, Cooperative control, Uncertain systems
Abstract: A distributed adaptive controller is proposed for a class of under-actuated Lagrangian systems under a directed communication graph to control the actuated variables to track a dynamic leader and keep unactuated ones bounded. A finite time observer is introduced to estimate the leader’s velocity. Based on the two sliding variables defined for the actuated and unactuated channels, the adaptive controllers are designed for the under-actuated Lagrangian systems with or without actuator faults. The convergence of the proposed controllers is proved based on the separation principle between the estimation and control. Finally, experiments are conducted to verify the effectiveness of the proposed controllers.

Keywords: Adaptive control, Identification for control
Abstract: Classical discrete-time adaptive controllers provide asymptotic stabilization and tracking; neither exponential stabilization nor a bounded noise gain is typically proven. In recent work it has been shown, in both the pole placement stability setting and the first-order one-step-ahead tracking setting, that if the original, ideal, Projection Algorithm is used (subject to the common assumption that the plant parameters lie in a convex, compact set and that the parameter estimates are restricted to that set) as part of the adaptive controller, then a linear-like convolution bound on the closed loop behaviour can be proven; this immediately confers exponential stability and a bounded noise gain, and it can be leveraged to provide tolerance to unmodelled dynamics and plant parameter variation. In this paper we extend the approach to the d-step-ahead adaptive controller setting and prove comparable properties.

Keywords: Intelligent systems, Adaptive control, LMIs
Abstract: In this paper, we propose a model reference adaptive control approach for uncertain dynamical systems in the presence of both actuator dynamics and actuator amplitude saturation limits. Specifically, we use a new expanded reference model including a deficit term between the ideal control signal and its saturated version. Closed-loop system stability of the proposed approach utilizing this expanded reference model is analyzed using linear matrix inequalities (LMIs) and Lyapunov theory, where the resulting stability conditions capture the interplay between the allowable actuator dynamics, actuator amplitude saturation limits, initial conditions, and the system uncertainties. An illustrative numerical example is further provided to demonstrate the efficacy of the proposed approach.

Keywords: Adaptive control, Lyapunov methods, Stability of nonlinear systems
Abstract: Adaptive tracking control problem for disturbed nonlinear systems with the time derivative of disturbance being unbounded is investigated in this paper. Different from the existing literatures, a new disturbance observer is constructively proposed with its parameters being functions rather than constants, which results in a new manner for our disturbance observer. The convergence of the new disturbance observer is then proved based on Lyapunov stability theorem. Moreover, it is proved that the tracking error of system can be regulated to arbitrary small by appropriately choosing the design functions and parameters. Finally, simulation results are given to demonstrate the effectiveness of designed method.

Keywords: Adaptive control, Direct adaptive control
Abstract: This paper considers MIMO feedback noise control in a three-dimensional acoustic space, where the plant may be wide, tall, or square. The goal is to investigate the implications of the plant aspect ratio within the context of retrospective cost adaptive control (RCAC), which is susceptible to canceling unmodeled nonminimum-phase transmission zeros. To obtain the necessary modeling information, each control speaker is impulsed, and the data from the resulting impulse response is used to construct the intercalated target model needed by RCAC. No additional system identification or analytical modeling is used for controller implementation. Laboratory experiments are used to evaluate the ability of RCAC to reject harmonic and broadband disturbances for wide, square, and tall sensor/actuator configurations.

Keywords: Adaptive control
Abstract: Adaptive control of linear systems with unknown nonminimum-phase (NMP) zeros remains a significant challenge. Although retrospective cost adaptive control (RCAC) is applicable to NMP systems with known NMP zeros, errors in the knowledge of those zeros can lead to unstable pole/zero cancellation under sufficiently aggressive tuning. To address this problem, this paper provides a numerical investigation of a heuristic extension of RCAC that exploits the propensity of RCAC to cancel NMP zeros, thereby inferring the NMP zeros. This modeling information can be subsequently incorporated within the target model used by RCAC. By focusing on only NMP zeros, this approach is distinct from conventional system identification, which relies on input-output data for model fitting. This cancellation-based technique relies on saturation of the control input and instability of the feedback controller. Simultaneous occurrence of control saturation and controller instability provides a heuristic indicator that the controller has cancelled one or more NMP zero during closed-loop operation. The estimated NMP zeros are subsequently used within a cancellation-based pseudo-identification extension of RCAC to prevent further cancellation of the NMP zeros.

Keywords: Observers for Linear systems, Switched systems, Optimization
Abstract: In this paper, we present a bounded-error estimator that achieves equalized recovery for discrete-time time-varying affine systems subject to missing data. By augmenting the system state estimate with a Luenberger-like observer error, we formulate the equalized recovery estimator design problem as a semi-infinite optimization problem, and leverage tools from robust optimization to solve it. Due to the design freedom introduced by the Luenberger-like observer, we can place the eigenvalues of the augmented system to desired locations, which results in a more optimal intermediate level in the equalized recovery problem than existing approaches in the literature. Furthermore, as an extension of the proposed equalized recovery estimator, we consider missing data in the estimator design, where a fixed-length language is used to specify the allowable missing data patterns. Simulation examples involving an adaptive cruise control system are given to demonstrate the equalized recovery performance of the proposed estimator.

Keywords: Observers for nonlinear systems, Control applications, Fluid power control
Abstract: This paper focuses on observer design for a dynamic system with non-monotonic nonlinear measurement equations. A Lyapunov-analysis-based observer design method for exponentially stable state estimation and a corresponding LMI for computing the observer design gains are developed. This observer design method is applied to linear position estimation using magnetic fields for smart industrial actuators. The output equations for magnetic field as a function of position are nonlinear and non-monotonic. A single constant gain that can satisfy the observer stability condition over the entire range of operating conditions does not exist, but a constant stabilizing gain does exist in each piecewise monotonic region. A methodology for discretizing the operating range into piecewise regions and using a finite state machine for switching between piecewise regions with stabilizing observer gains is developed. Experimental results are presented on the performance of the observer in accurately estimating linear position.

Keywords: Observers for nonlinear systems, Estimation, Optimization algorithms
Abstract: In this work, we propose a unifying framework in the space of probability measures for gradient-based and sampling-based moving-horizon estimation methods. We begin with an investigation of the classical notion of strong local observability of nonlinear systems and its relationship to optimization-based state estimation. We then present a general moving-horizon estimation framework for strongly locally observable systems, as an iterative minimization scheme in the space of probability measures. This framework allows for the minimization of the estimation cost with respect to different metrics and divergences. In particular, we consider two variants, which we name W_2-MHE and KL-MHE, where the minimization scheme uses the 2-Wasserstein distance and the KL-divergence respectively. The W_2-MHE yields a gradient-based estimator whereas the KL-MHE yields a particle filter, for which we investigate asymptotic stability and robustness properties. Stability results for these moving-horizon estimators are derived in the distributional setting, against the backdrop of the classical notion of strong local observability which, to the best of our knowledge, differentiates it from other previous works. We also present results from numerical simulations to demonstrate the performance of these estimators.

Keywords: Observers for nonlinear systems, Hybrid systems, Control over communications
Abstract: This paper constructs observers for linear and Lipschitz nonlinear systems with self-triggered measurements by utilizing the interval observer framework, which guarantees that an upper bound on the error between the estimated state and the actual state are known. The self-triggered observers are designed based on periodically sampled interval observers which guarantees a positive uniform minimum inter-sampling time. Convex programs for constructing the observer gains and the triggering functions are proposed. Simulation results are given to show the effectiveness of the proposed algorithms.

Keywords: Observers for nonlinear systems, Hybrid systems
Abstract: In this paper, we assume we are given an asymptotic observer whose dynamics are not written in the plant’s coordinates and whose implementation requires the inversion of an injective immersion at each time. To avoid these costly computations, we propose a method to write the observer dynamics directly in the plant’s coordinates by extending the injective immersion into a diffeomorphism and inverting its Jacobian. This is done by combining, in a hybrid way, those dynamics with an independent practical observer (maybe of smaller dimension), which is used to reset the estimate whenever it leaves the diffeomorphism domain where the Jacobian is invertible. This latter operation may necessitate to inverse an injective map, but we show that it happens only a finite number of times during the transient, and this inversion does not need to be exact : it can be done thanks to a minimization on a rough grid. The obtained observer is proved to be globally asymptotically convergent and robust to noise. Its performances are illustrated on a bioreactor model.

Keywords: Observers for nonlinear systems, Lyapunov methods
Abstract: This paper investigates the observer design for a class of nonlinear discrete-time systems satisfying incremental quadratic constraints. A circle criterion based full-order observer is constructed by injecting output estimation error into the observer nonlinear terms. We also construct a reduced-order observer to estimate the unmeasured system state. The proposed observers guarantee exponential convergence of the estimation error to zero. The design of the proposed observers is reduced to solving a set of linear matrix inequalities. It is proved that the conditions under which a full-order observer exists also guarantee the existence of a reduced-order observer. Compared to some previous results in the literature, this work considers a larger class of nonlinearities and unifies some related observer designs for discrete-time nonlinear systems. Finally, a numerical example is given to illustrate the effectiveness of the proposed design.

Keywords: Robust control, Biological systems, Networked control systems
Abstract: This paper describes several surprisingly rich but simple demos and a new experimental platform for human sensorimotor control research and also controls education. The platform safely simulates a canonical sensorimotor task of riding a mountain bike down a steep, twisting, bumpy trail using a standard display and inexpensive off-the-shelf gaming steering wheel with a force feedback motor. We use the platform to verify our theory, presented in a companion paper. The theory tells how component hardware speed-accuracy tradeoffs (SATs) in control loops impose corresponding SATs at the system level and how effective architectures mitigate the deleterious impact of hardware SATs through layering and 'diversity sweet spots' (DSSs). Specifically, we measure the impacts on system performance of delays, quantization, and uncertainties in sensorimotor control loops, both within the subject's nervous system and added externally via software in the platform. This provides a remarkably rich test of the theory, which is consistent with all preliminary data. Moreover, as the theory predicted, subjects effectively multiplex specific higher layer planning/tracking of the trail using vision with lower layer rejection of unseen bump disturbances using reflexes. In contrast, humans multitask badly on tasks that do not naturally distribute across layers (e.g. texting and driving). The platform is cheap to build and easy to program for both research and education purposes, yet verifies our theory, which is aimed at closing a crucial gap between neurophysiology and sensorimotor control. The platform can be downloaded at https://github.com/Doyle-Lab/WheelCon.

Keywords: Robust control, Game theory
Abstract: We consider output-feedback robust control of a linear system subject to disturbances and noise and in presence of an attacker who: 1) can corrupt the measured output (deception attack) and, 2) can introduce perturbations to the control signal (actuation attack). We consider an open-loop control problem over a finite horizon which models the scenario where feedback control could be stopped if one is certain that an attack is ongoing. We formulate this problem as a zero-sum game between a defender that selects the control signal based on a measured output and an attacker that selects the attack signals. The game has asymmetric information in that the defender only knows the measured output, whereas the attacker knows additional information, which includes the value of initial conditions and disturbances/measurement noise. The main contributions are (i) sufficient conditions for the existence of a Nash equilibrium corresponding to a saddle-point for the defender and (ii) a computationally efficient procedure to compute a pair of policies that form a Nash equilibrium for the game. We apply the procedure to a finite horizon linear quadratic control problem.

Keywords: Robust control, H-infinity control, Mechanical systems/robotics
Abstract: Negative imaginary (NI) systems are those characterized by a negative imaginary frequency response. A NI system connected in a positive feedback interconnection with a strictly negative imaginary (SNI) controller is internally stable if and only if a DC gain condition is satisfied. This can be interpreted as a robust stability result in situations where plant uncertainty does not destroy the NI nature of the plant nor the DC gain condition. Motivated by a desire to realize improved closed-loop performance, this paper considers the design of H ∞ -optimal SNI controllers. The proposed synthesis method makes use of convex optimization and linear matrix inequality (LMI) tools. Another contribution of this paper is pointing out how to realize tip position control, rather than joint position control, of a flexible manipulator within a NI framework. Numerical simulation results are included.

Keywords: Robust control, Linear parameter-varying systems, Uncertain systems
Abstract: This work investigates how to address box-type uncertainties in economic linear optimal control (ELOC). We consider two methods, a robust formulation for when the uncertainty is completely unknown and a Linear Parameter Varying formulation for when uncertainty can be measured in real time. In both cases, the infinite number of conditions that need to be satisfied are reduced to a finite set of constraints. The resulting problem formulations have a similar structure to the ELOC and can be solved globally by employing the generalized Benders decomposition.

Keywords: Robust control, Optimal control, Biological systems
Abstract: Nervous systems sense, communicate, compute, and actuate movement, using distributed hardware with tradeoffs in speed and accuracy. The resulting sensorimotor control is nevertheless remarkably fast and accurate due to highly effective layered architectures. However, such architectures have received little attention in neuroscience due to the lack of theory that connects the system and hardware level speed-accuracy tradeoffs. In this paper, we present a theoretical framework that connects the speed-accuracy tradeoffs of sensorimotor control and neurophysiology. We characterize how the component SATs in spiking neuron communication and their sensory and muscle endpoints constrain the system SATs in both stochastic and deterministic models. The results show that appropriate speed-accuracy diversity at the neurons/muscles levels allow nervous systems to improve the speed and accuracy in control performance despite using slow or inaccurate hardware. Then, we characterize the fundamental limits of layered control systems and show that appropriate diversity in planning and reaction layers leads to both fast and accurate system despite being composed of slow or inaccurate layers. We term these phenomena ``Diversity Sweet Spots.'' The theory presented here is illustrated in a companion paper, which introduces simple demos and a new inexpensive and easy-to-use experimental platform.

Keywords: Automotive control, Optimization, Estimation
Abstract: Public road traffic is rich in examples of dynamic objects suddenly appearing/disappearing in/from the Field of View (FoV) of an autonomous ego vehicle, such as when target vehicles zoom out by accelerating from the ego vehicle or sensor detections deteriorate temporarily due to shadows and other environmental effects. Thus, the guidance and control system should capture the motion of moving obstacles by a perception and tracking module capable of track management features including but not limited to track initiation and termination. This paper presents such a module that executes multi-target tracking with the linear integrated probabilistic data association (IPDA) filter in conjunction with a model predictive control (MPC) scheme for path and reference speed tracking and obstacle avoidance. From the perception module, all the confirmed tracks are made available to an Interacting Multiple Model (IMM) subsystem which predicts the motion of target vehicles to constrain the optimization problem in the MPC. The paper includes illustrations of the proposed scheme with practical traffic scenarios, which show that the ego vehicle is able to autonomously react to random changes in the number and/or state of target vehicles as well as to occasional missed detections due to environmental effects.

Keywords: Multivehicle systems
Abstract: Adding vehicle-to-vehicle (V2V) communication into the adaptive cruise control (ACC) system produces the cooperative adaptive cruise control (CACC) with a reinforced road safety. However, current policy obliges safety-related data to be exchanged solely over the control channel (CCH) within the Dedicated Short-Range Communication (DSRC) spectrum, which may induce intolerable communication delay and higher accident risks. Standard countermeasures concentrate principally on adaptively adjusting transmission parameters. However, due to the limited network capacity of a single channel, these methods can hardly meet the real-time packet delivery requirement when vehicle density becomes high. Therefore, to ensure timely delivery of critical safety messages, this paper proposes instead a dynamic channel selection algorithm to fully exploit all the seven useable channels in the DSRC band. Experiments on a two-scaled-car platoon demonstrate the effectiveness of the method in reducing the vehicle CACC position and speed tracking errors.

Keywords: Multivehicle systems, Robust control, Variable-structure/sliding-mode control
Abstract: Platooning has become one of the most appealing formations for intelligent vehicles safety enhancement and traffic regulation. Besides the traditional control algorithms, which are required to enforce at least local and string stability, more complex control schemes can be designed to cope with advanced requirements. In this paper, a suitable Distributed Model Predictive Control (DMPC) scheme, robustified with a second-order Integral Sliding Mode (ISM) correction term, is proposed to enforce and maintain coherence during cruising, while considering energy efficiency during acceleration/deceleration phases. While the former aspect has a complex impact on traffic regulation, especially when a large number of vehicles is considered, the latter is of primary importance in an increasingly eco-friendly transportation systems design. The proposed approach is well suited for real-world implementation, and can constitute a valid basis for more advanced control architectures. Simulation results highlight the effectiveness of the proposed architecture in maintaining the formation while guaranteeing a robust achievement of the required performance.

Keywords: Control applications, Automotive control, Stability of linear systems
Abstract: In this paper, we propose a mitigation scheme to prevent an attacker from causing collisions in a vehicular platoon under a gain modification attack. A control algorithm, based on fractional-order calculus and using only local sensor information, is shown to significantly alleviate the impact of the attacker. The control is incorporated into the system when the attacker(s) is(are) detected. We prove that once the stream has been destabilized and its states continually deviate from the desired trajectories, the attacker can be interrupted by another member of the platoon. Simulations demonstrate that by applying our proposed control method, collisions are eliminated despite the attacker maliciously altering its gain such that system becomes unstable or shows oscillatory behavior.

Keywords: Hybrid systems, Networked control systems, Automotive control
Abstract: The problem of designing a decentralized Cooperative Adaptive Cruise Control (CACC) with quantifiable robustness margins with respect to network delays and intermittent measurements is studied. A networked decentralized proportional-derivative controller is considered to achieve string stability for a platoon of vehicles. The closed-loop system is augmented with a timer triggering the arrival of new measurements. Sufficient conditions in the form of matrix inequalities are given to design the proposed controller with additional performance specifications. The effectiveness of the approach is shown in a numerical example.

Keywords: Automotive control, Optimization
Abstract: For a connected and autonomous vehicle (CAV), co-optimization of vehicle speed and powertrain operation maximizes the fuel benefits. For an internal combustion engine based vehicle (ICV), the transmission gear position can be optimized to adapt to anticipated future vehicle speed and power demand. It is necessary to consider drivability when optimizing the gear shift to ensure a satisfactory acceleration capability and to avoid the shift busyness. This work proposes a first-of-its-kind real-time implementable optimal control strategy to optimize vehicle speed and gear position simultaneously for ICVs while considering both fuel efficiency and drivability. The control strategy is developed upon a unified CAV framework so that it is widely applicable to various CAV applications. The optimal control problem is formulated and simplified to a mixed integer programming problem with a convex quadratic objective function and linear constraints. An efficient numerical solver is applied to obtain the optimal solutions for an eco-drive application in a model predictive control (MPC) fashion. The control is real-time implementable with an average computational time of 0.33 seconds and maximum computational time of 0.79 seconds. Results from simulation and experiment show that by co-optimizing vehicle speed and gear position, the target vehicle can achieve 16% fuel benefits compared to a baseline vehicle with constant speed cruising control. In addition, experimental results show that the optimal control can also significantly reduce emissions.

Keywords: Stochastic systems, Automotive systems, Fault detection
Abstract: A variety of anomaly detection schemes have been proposed to detect malicious attacks to Cyber-Physical Systems. Among these schemes, Dynamic Watermarking methods have been proven highly effective at detecting a wide range of attacks. Unfortunately, in contrast to other anomaly detectors, no method has been presented to design a Dynamic Watermarking detector to achieve a user-specified false alarm rate, or subsequently evaluate the capabilities of an attacker under such a selection. This paper describes methods to measure the capability of an attacker, to numerically approximate this metric, and to design a Dynamic Watermarking detector that can achieve a user-specified rate of false alarms. The performance of the Dynamic Watermarking detector is compared to three classical anomaly detectors in simulation and on a real-world platform. These experiments illustrate that the attack capability under the Dynamic Watermarking detector is comparable to those of classic anomaly detectors. Importantly, these experiments also make clear that the Dynamic Watermarking detector is consistently able to detect attacks that the other class of detectors are unable to identify.

Keywords: Stochastic systems, Formal verification/synthesis, Automata
Abstract: In hierarchical planning for Markov decision processes (MDPs), temporal abstraction allows planning with macro-actions that take place at different time scale in form of sequential composition. In this paper, we propose a novel approach to compositional reasoning and hierarchical planning for MDPs under co-safe temporal logic constraints. In addition to sequential composition, we introduce a composition of policies based on generalized logic composition: Given sub-policies for sub-tasks and a new task expressed as logic compositions of subtasks, a semi-optimal policy, which is optimal in planning with only sub-policies, can be obtained by simply composing sub-polices. Thus, a synthesis algorithm is developed to compute optimal policies efficiently by planning with primitive actions, policies for sub-tasks, and the compositions of sub-policies, for maximizing the probability of satisfying constraints specified in the fragment of co-safe temporal logic. We demonstrate the correctness and efficiency of the proposed method in stochastic planning examples with a single agent and multiple task specifications.

Keywords: Stochastic systems, Simulation, Biological systems
Abstract: Single particle tracking is a powerful tool for studying and understanding the motions of biological macromolecules integral to cellular processes. In the past three decades there has been continuous and rapid development of these techniques in both optical microscope design and in algorithms to estimate the statistics and positions of the molecule's trajectory. Although there has been great progress, comparison between different microscope configurations and estimation algorithms has been difficult beyond simulated data. In this paper we explore using a piezo actuated microscope stage to reproduce Brownian motion. Our goal is to use this as a tool to test performance of single particle tracking optical microscopes and estimation algorithms. In this study, Monte Carlo simulations were used to assess the ability of piezo actuated microscope stages for reproducing Brownian motion. Surprisingly, the dynamics of the stage together with configuration of the system allow for preservation of the Brownian motion statistics. Further, feed forward model inverse control allows for low error tracking of Brownian motion trajectories over a wide range of diffusion constants, varying stage response times, and trajectory discrete time steps. These results show great promise in using a piezo actuated microscope stage for testing single particle tracking experimental setups.

Keywords: Manufacturing systems, Distributed control, Stochastic systems
Abstract: In this paper, we deal with a distributed control of stock levels of buffers in a large-scale manufacturing factory. We suppose that the manufacturing factory is composed of many production processes and transportation processes. Each production process has several input buffers to stock materials for production temporarily and an output buffer to stock the produced materials temporarily. The transportation process is assumed to be realized by a swarm of vehicles, which flexibly move between buffers and transport the materials conveniently. We model the production processes as deterministic dynamical systems and the transportation processes as stochastic events. Then, we propose a distributed control method for the production rates and the transportation probabilities by employing an idea of consensus algorithms and rigorously prove that all the normalized stock levels of the buffers are globally stable at consensus values in a probabilistic sense. We finally demonstrate the effectiveness of our proposed method by numerical simulations.

Keywords: PID control, Constrained control, Stochastic systems
Abstract: Tracking loss due to zero mean measurement noise has been discovered in feedback control systems with saturating actuators. This phenomenon was named as “Noise-Induced Tracking Error (NITE)” and was analyzed for several types of feedback control systems in previous work. In this paper, we show that a similar phenomenon occurs in feedback control systems with saturating sensors. Specifically, in a class of PI controlled systems with saturating sensors, we show that NITE occurs, and quantify it, using stochastic averaging, in terms of system parameters and noise characteristics. The result is both qualitatively and quantitatively different from that for the systems with saturating actuators. Simulation results are provided for validation of analysis.

Keywords: Model/Controller reduction, Stochastic systems, Biomolecular systems
Abstract: To describe the stochastic behavior of biomolecular systems, the Chemical Master Equation (CME) is widely used. The CME gives a complete description of the evolution of a system's probability distribution. However, in general, the CME's dimension is very large or even infinite, so analytical solutions may be difficult to write and analyze. To handle this problem, based on the fact that biomolecular systems are time-scale separable, we approximate the CME with another CME that describes the dynamics of the slow species only. In particular, we assume that the number of each molecular species is bounded, although it may be very large. We thus write Ordinary Differential Equations (ODEs) of the slow-species counts' marginal probability distribution and of the fast-species counts' first n conditional moments. Here, n is an arbitrary (possibly small) number, which can be chosen to compromise between approximation accuracy and the computational burden associated with simulating or analyzing a high dimensional system. Then we apply conditional moment closure and time-scale separation to approximate the first n conditional moments of the fast-species counts as functions of the slow-species counts. By substituting these functions on the right-hand side of the ODEs that describes the marginal probability distribution of the slow-species counts, we can approximate the original CME with a lower dimensional CME. We illustrate the application of this method on an enzymatic and a protein binding reaction.

Keywords: Distributed parameter systems, Control of networks, Robust control
Abstract: We detail in this article the development of a delay-robust stabilizing state feedback control law for an underactuated network of two systems of two heterodirectional linear first-order hyperbolic Partial Differential Equations interconnected through their boundaries. Only one of the two subsystems is actuated. The proposed approach combines successive backstepping transformations which allow to rewrite the original network system as a simple neutral system with distributed delays for which the control design is easy. The proposed feedback law is finally proved to be robust to small delays in the actuation.

Keywords: Distributed parameter systems, Estimation, Grey-box modeling
Abstract: This paper presents a frequency domain approach to estimate spatially varying physical parameters of a one dimensional diffusion-transport-reaction equation. A non-linear least squares optimization of a frequency relevant criterion is proposed on the basis of measurements from a limited number of sensors. Analytic expressions of the Jacobian of the criterion function are exploited in an efficient numerical scheme. The proposed method exploits the sparsity of the underlying discretized model for a fast computation of the system parameters. The performance of the proposed procedure is illustrated by a number of simulation results. We demonstrate that the proposed method is able to estimate a spatially varying profile of unknown physical parameters in a realistic scenario.

Keywords: Distributed parameter systems, LMIs, Optimization
Abstract: In this paper, we propose an LMI-based approach to analyze input-output properties of coupled linear PDE systems. This work expands on a newly developed state-space theory for coupled PDEs and extends the positive-real and bounded-real lemmas to infinite dimensional systems. We show that conditions for passivity and bounded L₂ gain can be expressed as linear operator inequalities on RxL₂. A method to convert these operator inequalities to LMIs by using parameterization of the operator variables is proposed. This method does not rely on discretization and as such, the properties obtained are prima facie provable. We use numerical examples to demonstrate that the bounds obtained are not conservative in any significant sense and that the bounds are computable on desktop computers for systems consisting of up to 20 coupled PDEs.

Keywords: Distributed parameter systems, Mechatronics
Abstract: This contribution focuses on the optimal design and pneumatic deformation control of thin circular plates with spatially-varying thickness. The work is motivated by pneumatically actuated deformable mirrors, which are used to compensate for wavefront disturbances in adaptive optics. To achieve the required deformation profiles with high precision, the equilibrium deformation is optimized with respect to the plate’s thickness. For designing an asymptotic tracking control, the finite relative degree of the distributed parameter system is exploited. Since the nonlinear pressure dynamics actuate the governing partial differential equation (PDE) in a distributed way, finite-order feedback linearization can be applied. In addition, static nonlinear effects in the pneumatic valve and infinite-dimensional internal dynamics have to be taken into account. Finally, we demonstrate the controller's performance by means of simulations.

Keywords: Distributed parameter systems, Modeling, Chemical process control
Abstract: In this contribution we develop a distributed parameter model of a class of thermodynamic systems described by transport hyperbolic partial differential equations. The general model is proposed in a thermodynamically consistent framework, where the internal entropy production is positive semi-definite regardless if the system is far from equilibrium, i.e., the force-flux relations can be nonlinear and the internal entropy production can be associated to stability or passivity properties. A case study of an adiabatic plug flow reactor with stream recycle is analyzed from an entropy production point of view, showing the sensitivity of the steady internal entropy production density and state variables to the recycle rate and a possible link to instabilities.

Keywords: Power electronics, Control applications, Modeling
Abstract: The effective modeling and high speed digital control of variable frequency power converters has been a long-standing challenge. In this paper, we extend and analyze with proofs for stability and performance along with calculations for robustness, a switching-synchronized sampled-state space framework for a current-mode boost converter with constant off-time. We demonstrate a controller in this framework in both simulation and real-world hardware.

Keywords: Power electronics, Lyapunov methods, Control applications
Abstract: In this paper, a cascaded control scheme based on nonlinear methods has been designed to simultaneously improve the quality of the local load voltage while also controlling the injected grid current in a grid-connected single-phase inverter system. This control approach ensures the seamless transfer between grid-connected and stand-alone operation modes without adjusting the controller structure. The proposed control structure consists of an outer current loop and inner voltage loop, each of which are motivated by separate Lyapunov based stability analysis. In an effort to reduce cost and noise sensitivity an inductor current observer is utilized. This scheme incorporates a Learning scheme to compensate for periodic disturbances which are present in the dynamic system. Moreover, since the impedance of the grid has significant effect on system stability and current control performance, parameter estimation scheme is developed to compensate for this unknown parameter. Each scheme in the cascaded system is validated through a Lyapunov stability analysis. The overall scheme is validated with an instantaneous circuit simulation where PLECS was utilized.

Keywords: Power electronics, Robust control, Constrained control
Abstract: This work presents the design of state-feedback robust control laws for boost power converters. First, an equivalent circuit of the boost converter is presented and a nonlinear(bilinear) state-space representation is obtained. The system is linearized around an equilibrium point, such that linear robust control techniques can be applied. An integral action is added to the linearized system, such that step-like disturbances can be rejected in closed-loop. A saturation restriction on the control signal is then imposed to limit the operation of the system in a region where the linear model approximation is valid. In order to improve the closed-loop performance, an Hinf control design with pole placement restrictions based on linear matrix inequalities is considered. Finally, a static antiwindup compensator is designed to reduce possible integral windup effects imposed by the saturation. The resulting control system is validated by PSIM simulation software, where high frequency switching and other unmodeled effects neglected in the modeling stage are considered.

Keywords: Control applications, Power electronics, Observers for nonlinear systems
Abstract: In this paper, a backstepping nonlinear control scheme has been designed for a single-phase grid-connected inverter system with local load. The main objective of the proposed controller is to simultaneously improve the quality of the local load voltage while injecting clean current to the grid with unity power factor. Furthermore, this control approach ensures the seamless transfer between grid-connected and stand-alone operation modes without adjusting the controller structure. The proposed control structure utilizes a variable structure nonlinear observer to avoid using the numerical derivative of the output voltage. Moreover, the variable structure component of the observer makes it robust to a wide range of operating conditions. The proposed combination of controller/ observer is validated by a Lyapunov stability analysis as well as via instantaneous dynamic circuit simulation in PLECS software.

Keywords: Power systems, Distributed control, Control of networks
Abstract: In this paper, we propose a new distributed passivity-based control strategy for Direct Current (DC) microgrids. The considered DC microgrid includes Distributed Generation Units (DGUs) sharing power through resistive-inductive distribution lines. Each DGU is composed of a generic DC energy source that supplies an unknown load through a DC-DC buck converter. The proposed control scheme exploits a communication network, the topology of which can differ from the topology of the physical electrical network, in order to achieve proportional (fair) current sharing using a consensus-like algorithm. Moreover, the proposed distributed control scheme regulates the average value of the network voltages towards the corresponding desired reference, independently of the initial condition of the controlled microgrid. Convergence to a desired steady state is proven and satisfactorily assessed in simulations.

Keywords: Power systems, Distributed control, Predictive control for linear systems
Abstract: This paper proposes a control strategy for power systems with a two-layer structure that achieves global stabilization and, at the same time, delimits the transient frequencies of targeted buses to a desired safe interval. The first layer is a model predictive control that, in a receding horizon fashion, optimally allocates the power resources while softly respecting transient frequency constraints. As the first layer control requires solving an optimization problem online, it only periodically samples the system state and updates its action. The second layer control, however, is implemented in continuous time, assisting the first layer to achieve frequency invariance and attractivity requirements. Furthermore, through network partition, they can be implemented in a distributed fashion, only requiring system information from neighboring partitions. Simulations on the IEEE 39-bus network illustrate our results.

Keywords: Robotics, Mechanical systems/robotics, Biomedical
Abstract: Energy shaping methods can be used to design task-invariant feedback control laws for the powered exoskeletons (i.e., orthoses). In order to achieve a desired closed-loop energy, certain matching conditions must be satisfied, which are sets of nonlinear partial differential equations. In this paper, we solve the matching conditions and come up with a new solution for under-actuated systems by using Auckly's method. We find a unified feedback control law that is task-invariant with respect to human inputs and different contact conditions. We propose assistive and resistive shaping strategies to alter the mass/inertia matrix and simulate on a powered knee-ankle exoskeleton. The simulation results show the reduction and increment of the human model's metabolic cost of generating muscular forces in human walking. The interchange between the kinetic and potential energy and the changes in acceleration of the center of mass are also investigated in the simulation.

Keywords: Robotics, Mechanical systems/robotics, Constrained control
Abstract: This paper presents a trajectory generation method for contact-constrained robotic systems such as manipulators and legged robots. Contact-constrained systems are affected by the interaction forces between the robot and the environment. In turn, these forces determine and constrain state reachability of the robot parts or end effectors. Our study subdivides the trajectory generation problem and the supporting reachability analysis into tractable subproblems consisting of a sampling problem, a convex optimization problem, and a nonlinear programming problem. Our method leads to significant reduction of computational cost. The proposed approach is validated using a realistic simulated contact-constrained robotic system.

Keywords: Robotics, Mechanical systems/robotics
Abstract: This paper presents control strategies to ensure that the tangential velocity of a robot, which is commanded to move in a trajectory with a constant offset from a reference curve, is bounded within a specified range. This is achieved at the expense of adjusting the distance between the robot and the curve. The strategies are based on zeroing control barrier functions and minimum norm control inputs, which ensure the satisfaction of constraints while only deviating from the nominal trajectory when necessary. Control laws for curves on a plane and in 3D space are presented, using the Frenet-Serret and Bishop frames to specify the desired trajectory of the robots. We illustrate our results through simulations.

Keywords: Robotics, Modeling, Biotechnology
Abstract: Tissue engineering (TE) has seen success in recapitulating the natural function of a variety of simple tissues in the laboratory setting. One barrier to increased clinical translation of tissue constructs is morbidities caused by open surgeries currently needed for their delivery into the body. Advanced robotics and control allow for new tools and manufacturing capabilities that can accelerate the clinical viability of existing forms of TE today. One such tool, an intracorporeal, additive manufacturing (AM) based TE fabrication system in an endoscopic form factor, the Endo AM system, allows for the fabrication of TE constructs inside the body in a minimally-invasive manner. The Endo AM system consists of a 9-joint robotic manipulator and a direct-write (DW) AM extruder, leading to complex flow and positioning dynamics. Here we describe and explore the dynamics of the Endo AM system in simulation, with a focus on studying the coupling of dynamic positioning and material delivery axes.

Keywords: Robotics, Robust control, Mechanical systems/robotics
Abstract: Tactile-based blind grasping refers to a realistic grasping scenario where the robotic hand has no knowledge of the object, and only has access to on-board sensors. This scenario is representative of real-world applications (e.g prosthetics) where high resolution cameras and other external sensors are not available to the robotic hand as they would be in a structured, laboratory setting. At present, there is no manipulation control that can track a reference object trajectory in tactile-based blind grasping. In this paper, a robust trajectory tracking controller is proposed to enhance the manipulation capabilities of robotic hands in tactile-based blind grasping. The proposed control ensures semi-global practical asymptotic tracking of the reference trajectory, while compensating for bounded wrench disturbances. Numerical simulations show the efficacy of the proposed approach.

Keywords: Robotics, Stability of hybrid systems
Abstract: A hybrid controller for stabilization of homoclinic orbits of two degree-of-freedom (DOF) underactuated systems is proposed. The controller is comprised of continuous-time inputs, impulsive brakings, and virtual impulsive inputs for resetting of the passive coordinate. Impulsive brakings of the active coordinate result in instantaneous negative changes in the mechanical energy of the system. An impulsive dynamical system framework is adopted for modeling the hybrid dynamics and a Lyapunov function is defined for stabilization of the orbit. Sufficient conditions for stabilization are presented such that the Lyapunov function decreases monotonically under the action of the continuous inputs and undergoes negative jumps due to impulsive brakings. The control design is implemented on an inverted pendulum on a cart example. Simulation results indicate fast convergence of system trajectories to the homoclinic orbit corresponding to the upright equilibrium configuration.

Keywords: Automotive control, Lyapunov methods, Autonomous systems
Abstract: Trajectory planning is challenging for autonomous cars since they operate in unpredictable environments with limited sensor horizons. To incorporate new information as it is sensed, planning is done in a loop, with the next plan being computed as the previous plan is executed. Reachability- based Trajectory Design (RTD) is a recent, provably safe, real- time algorithm for trajectory planning. RTD consists of an offline Forward Reachable Set (FRS) computation of the vehi- cle tracking parameterized trajectories; and online trajectory optimization using the FRS to map obstacles to constraints in a provably-safe way. In the literature, RTD has only been applied to small mobile robots. The contribution of this work is RTD on a passenger vehicle in CarSim, with a full powertrain model, chassis and tire dynamics. RTD operates the vehicle safely at up to 15 m/s on a two-lane road around randomly- placed obstacles only known to the vehicle when detected within its sensor horizon. RTD is compared with a Nonlinear Model- Predictive Control (NMPC) and a Rapidly-exploring Random Tree (RRT) approach. The experiment demonstrates RTD’s ability to plan safe trajectories in real time, in contrast to the existing state-of-the-art approaches.

Keywords: Automotive control, Mechanical systems/robotics, Variational methods
Abstract: The autonomous vehicle is one of the greatest challenges in modern vehicle design. This paper proposes a new method of control named FLOP, Feedback Local Optimality Principle, recently proposed by the authors. The method, starting from the Pontryagin’s theory, introduces a new optimality principle that minimizes a sequence of individual functionals with the chance of a direct feedback control. The theory is applied to the steering and traction control of a two-wheeled vehicle, showing the ability of tracking a given trajectory and obstacles avoidance in a rather complex environment.

Keywords: Automotive control, Predictive control for linear systems, Autonomous systems
Abstract: We present Contingency Model Predictive Control (CMPC), a novel and implementable control framework which tracks a desired path while simultaneously maintaining a contingency plan – an alternate trajectory to avert an identified potential emergency. In this way, CMPC anticipates events that might take place, instead of reacting when emergencies occur. We accomplish this by adding an additional prediction horizon in parallel to the classical receding MPC horizon. The contingency horizon is constrained to maintain a feasible avoidance solution; as such, CMPC is selectively robust to this emergency while tracking the desired path as closely as possible. After defining the framework mathematically, we demonstrate its effectiveness experimentally by comparing its performance to a state-of-the-art deterministic MPC. The controllers drive an automated research platform through a left-hand turn which may be covered by ice. Contingency MPC prepares for the potential loss of friction by purposefully and intuitively deviating from the prescribed path to approach the turn more conservatively; this deviation significantly mitigates the consequence of encountering ice.

Keywords: Intelligent systems, Optimization, Automotive systems
Abstract: In the autonomous driving area, interaction between vehicles is still a piece of puzzle which has not been fully resolved. The ability to intelligently and safely interact with other vehicles can not only improve self driving quality but also be beneficial to the global driving environment. In this paper, a Bayesian persuasive driving algorithm based on optimization is proposed, where the ego vehicle is the persuader (information sender) and the surrounding vehicle is the persuadee (information receiver). In the persuasion process, the ego vehicle aims at changing the surrounding vehicle's posterior belief of the world state by providing certain information via signaling in order to achieve a lower cost for both players. The information received by the surrounding vehicle and its belief of the world state are described by Gaussian distributions. Simulation results in several common traffic scenarios are provided to demonstrate the proposed algorithm's capability of handling interaction situations involving surrounding vehicles with different driving characteristics.

Keywords: Automotive control, Cooperative control, Lyapunov methods
Abstract: The time-gap regulation problem for a cascaded system consisting of platooned automated vehicles following a leading non-automated vehicle is investigated in this article. Under the assumption of uniform boundedness of the acceleration of the leading vehicle, a control design scheme is proposed via an extension of integral backstepping control method, where additional terms that counter the impact due to the speed change of the non-automated vehicle are used. Each automated vehicle is actuated by one backstepping controller, demonstrated by a recursive control design procedure based on induction. As a result, both the time-gap error and the speed error between each pair of consecutive vehicles are proven to be ultimately bounded by some constants that can be tuned to be arbitrarily close to zero. In particular, the regulated time-gap guarantees enough time for the following vehicle in each pair to react to the velocity change of its preceding vehicle. Simulation is carried out to validate the proposed controllers.

Keywords: Automotive systems, Automotive control, Intelligent systems
Abstract: Forecasting the speed trajectories of driving vehicles is essential for vehicle/powertrain predictive optimal control. This paper proposes a simple and effective forecasting method for generating short-term future speed trajectories using vehicle-to-vehicle (V2V) information. Specifically, a series of lead vehicles’ speeds and locations are considered to be the potential trajectories that the following car would drive in the near future. Polynomial regression based on weighted least-squares estimation is used to determine a future speed trajectory over a short prediction horizon. The efficacy of the proposed approach is evaluated in single-lane traffic simulations over various driving scenarios. In addition, the performance of the proposed method is also evaluated when V2V is not available. Simulation results show that for a highway drive cycle, the proposed predictor results in root-mean-square errors less than 1 mph with V2V data.

Keywords: Distributed control, Large-scale systems, Building and facility automation
Abstract: This paper presents a new methodology for distributed model predictive control of large-scale systems. The methodology involves two distinct stages, i.e., the decomposition of large-scale systems into subsystems and the design of subsystem controllers. Two procedures are used: in the first stage, the structure of the Karush-Kuhn-Tucker matrix resulting from the necessary optimality conditions is exploited to yield a decomposition of the large-scale system into several subsystems. In the second stage, a particular technique, the so-called optimality condition decomposition makes it possible to synthesize distributed coordinated subcontrollers thus achieving an optimal distributed control of the large-scale system. The convergence of the proposed approach is stated.

Keywords: Distributed control, Large-scale systems, Optimization
Abstract: In this paper, quasi-norms are utilized to sparsify a pre-given well-performing state-feedback controller stabilizing a linear time-invariant (LTI) system. To do so, an unconstrained optimization problem is firstly formulated which incorporates two terms: (i) the Frobenius norm of difference of the pre-given feedback controller and the one to be designed; (ii) the 0 < q < 1 quasi-norm of the feedback controller to be designed. The former term heuristically features the disturbance attenuation performance and the latter term promotes the sparsity. Next, obtaining an analytic threshold for the sparsity-promoting parameter, the analytic solution of the formulated unconstrained optimization problem is expressed which is basically the designed sparse feedback controller. Throughout the numerical simulations, it is observed that when 0 < q < 1 decreases, the sparsity-performance balance is significantly improved. Furthermore, the proposed method is interestingly capable of being applied to the large-scale systems with thousands of states.

Keywords: Distributed control, Network analysis and control, Agents-based systems
Abstract: Stability and analysis of networked decision systems with state-dependent switching typologies have been a fundamental and longstanding challenge in control, social sciences, and many other related fields. These already complex systems become further complicated once one accounts for asymmetry or heterogeneity of the underlying agents/dynamics. Despite extensive progress in analysis of conventional networked decision systems where the network evolution and state dynamics are driven by independent or weakly coupled processes, most of the existing results fail to address decision systems where the network and state dynamics are highly coupled and evolve based on status of heterogeneous agents. Motivated by numerous applications of such dynamics in social sciences, in this paper we provide a new direction toward analysis of dynamic networks of heterogeneous agents under complex environments. As a result we show how Lyapunov stability of several problems from opinion dynamics can be established using a simple application of our framework. Our results provide new insights toward analysis of complex networked multi-agent systems using exciting field of sequential optimization.

Keywords: Distributed control, Network analysis and control
Abstract: We study the problem of distributed consensus in networks where the local agents have high-order (n ≥ 3) integrator dynamics, and where all feedback is localized in that each agent has a bounded number of neighbors. We prove that no consensus algorithm based on relative differences between states of neighboring agents can then achieve consensus in networks of any size. That is, while a given algorithm may allow a small network to converge to consensus, the same algorithm will lead to instability if agents are added to the network so that it grows beyond a certain finite size. This holds in classes of network graphs whose algebraic connectivity, that is, the smallest non-zero Laplacian eigenvalue, is decreasing towards zero in network size. This applies, for example, to all planar graphs. Our proof, which relies on Routh-Hurwitz criteria for complex-valued polynomials, holds true for directed graphs with normal graph Laplacians. We survey classes of graphs where this issue arises, and also discuss leader-follower consensus, where instability will arise in any growing, undirected network as long as the feedback is localized.

Keywords: Distributed control, Networked control systems, Agents-based systems
Abstract: In this paper, we consider the problem of coordinating a multi-agent team to maintain a connected topology while equipped with limited field of view sensors. Applying the potential-based control framework and assuming agent interaction is encoded by a triangular geometry, we derive a distributed control law based on two non-linear functions, the point-to-line distance and the multivariate Gaussian, in order to achieve the topology control objective. Furthermore, we demonstrate a condition on digraphs for which the proposed control strategy achieves the system objective. Finally, numerical simulations are provided to corroborate the theoretical findings.

Keywords: Distributed control, Networked control systems, Machine learning
Abstract: Control of large-scale networked systems often necessitates the availability of complex models for the interactions amongst the agents. However, in many applications building accurate models of these interactions might be prohibitive due to the curse of dimensionality or their inherent complexity. In the meantime, data-guided control methods can circumvent model complexity by directly synthesizing the controller from the observed data. In this paper, we propose a distributed Q-learning algorithm to design a feedback mechanism given an underlying graph structure parameterizing the agents' communication. We assume that the distributed nature of the system arises from a common cost and show that for the particular case of identical dynamically decoupled systems, the learned controller converges to the optimal Linear Quadratic Regulator controller for each subsystem. We provide a convergence analysis and verify the result with an example.

Keywords: Networked control systems, Distributed control, Agents-based systems
Abstract: This paper investigates the problem of multi- agent consensus control with input disturbances. We propose a stochastic distributed event-triggereing law, based on an existing deterministic law, for the said problem. It is shown that both event-triggering laws achieve practical consensus exponentially fast under the influence of disturbance. We further derive an analytical upper bound for the consensus error and show that the stochastic law excludes Zeno behavior. A numerical simulation is also provided to illustrate the proposed consensus control law compared with the deterministic law.

Keywords: Networked control systems, Information theory and control, Estimation
Abstract: In this paper, we introduce the problem of motion planning with secrecy guarantees. A robot is tracking a desired trajectory, which is transmitted on-line by a planner, e.g. a base station or a mobile station. The communication between the robot and the planner is organized in packets and takes place over a wireless channel, which is susceptible to eavesdropping attacks. Our goal is to design secure communication codes in order to encode the trajectory information and hide it from any eavesdroppers. Meanwhile, the robot should be able to recover the trajectory and the planner should be able to estimate the robot's motion. We introduce a novel coding scheme that creates secrets between the robot and the planner based on i)~the randomness of the robot's motion and ii)~the imperfection of the communication channel. We show that every time the planner receives the corresponding packet while the eavesdropper misses it, a new secret is created, which can be used as a key to encode the information about the motion intent. If the motion-planning is random enough, one occurrence of this event makes the eavesdropper lose track of the trajectory; even if the eavesdropper has unlimited computational power. We apply our framework to the problem of way-point tracking, where the robot should visit some target positions one after the other. We illustrate the theoretical results in simulations.

Keywords: Networked control systems, Learning
Abstract: In networked control systems, communication is a shared and therefore scarce resource. Event-triggered control (ETC) can achieve high performance control with a significantly reduced amount of samples compared to classical, periodic control schemes. However, ETC methods usually rely on the availability of an accurate dynamics model, which is oftentimes not readily available. In this paper, we propose a novel event-triggered pulse control strategy that learns dynamics models if necessary. In addition to adapting to changing dynamics, the method also represents a suitable replacement for the integral part typically used in periodic control.

Keywords: Networked control systems, Linear parameter-varying systems, Stability of linear systems
Abstract: In order to overcome the constraints of Networked Control Systems (NCS) such as random packet delays or disorders, it is a natural idea to estimate the system state and compensate for the time delays on the controller side. However, it is rarely studied how this idea changes the structure and scale of the system. This paper provides an estimate on the scale of the complete dynamical system using this idea of control. The structure of the complete system is clearly illustrated. Then a concise sufficient and necessary stability condition is provided. In the numerical example, it is shown that a seemingly small system turns out to have a very large scale if random time delays and state estimates are present.

Keywords: Networked control systems, Optimization, LMIs
Abstract: We address the problem of stabilizing a system, with a certain desired dynamic performance, using control signal sent over a communication channel having a limited capacity. A desired dynamic performance of the closed-loop system is obtained by placing its poles in a specific region of the open left-half of the complex plane. Denoting such a region by D, this procedure is also called D-stabilization. We first analyze a single-input linear time-invariant (LTI) system with state-feedback control over a channel subjected to additive noise. Using tools from H₂ control theory, we pose the above stabilization problem as an optimization problem involving linear matrix inequalities (LMIs). Using this, we derive a sufficient condition for D-stabilization of the system subject to the channel capacity constraint. Further, we extend our analysis to a multi-input LTI system with state-feedback over a multi-input multi-output (MIMO) channel subjected to additive noise. The channel consists of multiple single-input single-output (SISO) subchannels connected in parallel. The total capacity of the MIMO channel is fixed, while the individual subchannel capacities can be freely allocated. We provide a necessary and sufficient condition for stabilization and a sufficient condition for D-stabilization of this system subject to the channel capacity constraints. We also propose a method to design the channel and an optimal controller.

Keywords: Networked control systems, Predictive control for nonlinear systems, Network analysis and control
Abstract: We consider a wireless control architecture with multiple control systems communicating over two shared collision channels. Each sensor accesses the channel randomly and a scheduler at the controller side decides which controller is permitted to access the channel. We design a packet-based model predictive controller and obtain the packet transmission success probability requirements of stability. The channel-aware transmission strategy of each sensor is designed and analyzed in the non-cooperative game theory framework. We also characterize the Nash equilibrium and design a decentralized channel access mechanism to achieve the Nash equilibrium. The effectiveness of our results is demonstrated by a numerical simulation.

Keywords: Optimization, Optimal control, Optimization algorithms
Abstract: This paper considers the optimization of multispeed transmission configuration and gearshift schedule for the battery electric vehicles. Although only a single reduction gear is commonly utilized in commercial battery electric vehicles, further improvements in energy savings can be achieved by employing a multi-speed transmission as has been already shown in existing literature. In this paper, we propose an approach to co-optimization of transmission design (number of gears and gear ratios) and of gear shift schedule. The simulation results quantify the battery energy savings which are achieved by the proposed approach and the ability to obtain shift schedules which promote regenerative braking.

Keywords: Optimization, Optimal control, Transportation networks
Abstract: Map matching is the process of matching a time sequence of location coordinate points into a digital road map. Currently there are different map matching algorithms designed for different types of location data with different frequencies, different accuracies, and different types of additional information that can be used to improve the results. However, there is not yet a universal map matching framework that considers all the map matching criteria and covers all these different scenarios. This paper presents a batch map matching framework that works well for high-frequency, high-accuracy location data and low-frequency low-accuracy location data, and it can consider different types of additional data for data fusion. The map matching problem is formulated as an optimal control problem for a discrete-time domain dynamic system, and the problem can be solved using dynamic programming. This framework will greatly benefit the map matching application development, as the core building blocks for the algorithm are the same for all types of data.

Keywords: Optimization, Optimization algorithms, Autonomous systems
Abstract: We present a path planning problem for a pursuer to intercept a cooperating target traveling on a circle. The pursuer considered here has limited yaw rate, and therefore its path should satisfy the kinematic constraints. We assume that the distance between initial position of the pursuer and any point on the target circle is greater than four times the minimum turn radius of the pursuer. We prove the continuity of the length of the Dubins paths of type Circle-Straight line-Circle with respect to the position on the target circle. This is used to prove that the optimal interception path is a Dubins path, and we present an iterative algorithm to find the optimal interception point on the target circle.

Keywords: Optimization, Optimization algorithms, Distributed control
Abstract: In this work, we propose a novel primal-dual algorithm for solving distributed optimization problems with consensus constraints over a network of agents. Our starting point is to form the Lagrangian of the constrained problem. The agents update their dual variables using dual gradient ascent (the dual step). By interpreting the dual variables as time-varying parameters, the agents track the minimizer of the resulting time-varying Lagrangian using a prediction-correction scheme (the primal step). Each iteration of the resulting algorithm requires two rounds of communication and two local Hessian inversions per agent. In particular, we establish exponential convergence of the resulting algorithm to a neighborhood of the optimal solution. Numerical experiments support the theoretical conclusions.

Keywords: Optimization, Optimization algorithms, Modeling
Abstract: This paper proposes a method of generating piece-wise linear trajectories with smoothed corners that optimizes two objectives: energy consumption and cornering tolerance for feed drive systems. An energy model of an industrial bi-axial feed drive system is used to formulate a bi-objective optimization problem. The linear and smooth corner segments are generated using jerk limited acceleration profiles and kinematic corner smoothing technique with interrupted acceleration, respectively. The optimization problem is described with normalized normal constraints, where sequential quadratic programming is used to solve it. A divide and conquer algorithm is utilized to recursively generate Pareto optimal solutions. The best trade-off solution is obtained as the one that minimizes both objectives. Optimization results for an industrial bi-axial machine are illustrated, where the best trade-off solution achieves 63.96% of the energy saving potential with a moderate cornering tolerance of 29.79μm.

Keywords: Biological systems, Biologically-inspired methods, Neural networks
Abstract: We propose a biologically plausible learning algorithm to train a neural circuit model to perform context-dependent associations of stimuli with correct responses. The specific cognitive task we consider requires the ability to learn a context-dependent association rule and generalize beyond what has been seen during training. We analyze the learning algorithm using a Markov chain framework and establish its convergence. Using numerical simulation, we validate the performance of the learning algorithm and the generalization ability of the neural circuit model.

Keywords: Lyapunov methods, Switched systems, Biomedical
Abstract: Functional electrical stimulation (FES) has proven to be an effective method for improving health and regaining muscle function for people with limited or reduced motor skills. Closed-loop control of motorized FES-cycling can facilitate recovery. Many people with movement disorders (e.g., stroke) have asymmetries in their motor control, motivating the need for a closed-loop control system that can be implemented on a splitcrank cycle. In this paper, nonlinear sliding mode controllers are designed for the FES and electric motor on each side of a split-crank cycle to maintain a desired cadence and a crank angle offset of 180 degrees, simulating standard pedaling conditions. A Lyapunov-like function is used to prove stability and tracking of the desired cadence and position for the combined cycle-rider system. One experimental trial on an able-bodied individual demonstrated the feasibility and stability of the closed-loop controller, which resulted in an average cadence error of 2.62 +/ 3.54 RPM for the dominant leg and an average position and cadence error of 39.84 +/ 10.77 degrees and -0.04 +/ 8.79 RPM for the non-dominant leg.

Keywords: Biomedical, Modeling, Estimation
Abstract: Deep brain stimulation (DBS) is a an established therapy in neurological and mental disorders making use of electrical pulses chronically delivered to a certain disease-specific neural target through surgically implanted electrodes. The therapeutical effect of DBS is highly individual and depends on the target coverage by the stimuli and the amount of spill beyond it. This can be suitably formulated as an optimization problem. Since the biological mechanism underlying the DBS therapy is mainly unknown, and due to high inter-patient and intra-patient variability of the DBS effect, a pragmatic approach to the DBS programming is to consider the process as tuning of a control system for the symptoms. Such a technology assumes that the symptoms are accurately quantified. The paper summarizes the progress in the individualized DBS and presents the results of a limited clinical study making use of the proposed DBS programming approach.

Keywords: Information theory and control, Learning, Optimization algorithms
Abstract: Abstract—Neurostimulation – the practice of applying exogenous excitation, e.g., via electrical current, to the brain – has been used for decades in clinical applications such as the treatment of motor disorders and neuropsychiatric illnesses. Over the past several years, more emphasis has been placed on understanding and designing neurostimulation from a systems-theoretic perspective, so as to better optimize its use. Particular questions of interest have included designing stimulation waveforms that best induce certain patterns of brain activity while minimizing expenditure of stimulus power. The pursuit of these designs faces a fundamental conundrum, insofar as they presume that the desired pattern (e.g., desynchronization of a neural population) is known a priori. In this paper, we present an alternative paradigm wherein the goal of the stimulation is not to induce a prescribed pattern per se, but rather to simply improve the functionality of the stimulated circuit/system. Here, the notion of functionality is defined in terms of an information-theoretic objective. Specifically, we seek closed loop control designs that maximize the ability of a controlled circuit to encode an afferent ‘hidden input,’ without prescription of dynamics or output. In this way, the control attempts only to make the system ‘effective’ without knowing beforehand the dynamics that are needed to be induced. We devote most of our effort to defining this framework mathematically, providing algorithmic procedures that demonstrate its solution and interpreting the results of this procedure for simple, prototypical dynamical systems. Simulation results are provided for more complex models, including an example involving control of a canonical neural mass model.

Keywords: Observers for nonlinear systems, Control applications, Sensor fusion
Abstract: Robotic devices and functional electrical stimulation (FES) are utilized to provide rehabilitation therapy to persons with incomplete spinal cord injury. The goal of the therapy is to improve their weakened voluntary muscle strength. A variety of control strategies used in these therapies need the measurement of the subject's volitional strength. This informs the robot-based or FES-based device to modulate assistance proportional to the patient's weakness. In this paper we propose an observer design to estimate ankle joint's kinematics that are elicited volitionally. The observer is designed for a nonlinear continuous-time neuromusculoskeletal system, which has multi-rate sampled output measurements with non-uniform and unknown delays that come from two sensing modalities including ultrasound imaging and inertial measurement unit. The allowable maximum values of unsynchronized sampling intervals and non-uniform delays are hypothesized, respectively. By constructing a Lyapunov-Krasovskii function, sufficient conditions are derived to show the exponential stability of the estimation error. At last, numerical simulations are provided to verify the effectiveness of the designed observer.

Keywords: Control applications, Biomedical, Systems biology
Abstract: Fractional-order dynamical systems are used to describe processes that exhibit long-term memory with power-law dependence. Notable examples include complex neurophysiological signals such as electroencephalogram (EEG) and blood-oxygen-level dependent (BOLD) signals. When analyzing different neurophysiological signals and other signals with a different origin (for example, biological systems), we often find the presence of artifacts, that is, recorded activity due to external causes having origins outside the system of interest. In this paper, we consider the problem of estimating the states of a discrete-time fractional-order dynamical system when there are artifacts present in some of the sensor measurements. Specifically, we provide necessary and sufficient conditions that ensure we can retrieve the system states even in the presence of artifacts. We provide a state estimation algorithm that can estimate the states of the system in the presence of artifacts. Finally, we present illustrative examples of our main results using real EEG data.

Keywords: Energy systems, Optimal control, Identification for control
Abstract: Open circuit voltage (OCV) is an important parameter of a battery model. In order to provide accurate state estimation and control command, the battery model parameters have to be calibrated regularly when the battery ages or the model prediction deviates from the data. In this study, an innovative method is developed to reduce the total testing time for taking incremental OCV measurements. In the traditional incremental OCV measurement, the relaxation period is the most time-consuming step waiting until the slow time constant of diffusion dynamics reaches equilibrium. Here, an optimal control problem is formulated to find the minimum-time current profile such that it brings the voltage across the double RC pairs to equilibrium and the initial SOC to target SOC while satisfying the state and input constraints. It is shown by both simulation and experiment that the proposed minimum-time current profile can reduce the total testing time by 69%.

Keywords: Estimation, Identification, Energy systems
Abstract: Recognizing the important role of data in estimation, data optimization for offline and online battery state and parameter estimation has been receiving increasing attention recently. The main idea is to design/select data that are most sensitive to the target variables under estimation. However, due to the lack of efficient ways to compute sensitivity and heavy reliance on simulation, data optimization is often intractable because of the computational complexity. This issue is especially prominent for the states and parameters of the electrochemical battery models. In this work, we study the methodology for analytically deriving the sensitivity of battery electrochemical parameters. The derivation is based on a single particle model, and the results have been verified by comparing to the numerical simulation of a full order pseudo-2D electrochemical model. The obtained analytic results provide theoretic insight on the dynamic nature of the parameter sensitivity. The derived analytic expressions could enable fast sensitivity computation to serve the data optimization for offline system identification and online data selection/mining for real-time estimation.

Keywords: Identification, Energy systems, Nonlinear systems identification
Abstract: Battery parameter identification is emerging as an important topic due to the increasing use of battery energy storage. This paper studies parameter identification for the nonlinear double-capacitor (NDC) model for lithium-ion batteries, which is a new equivalent circuit model developed in the authors' previous work. It is noticed that the NDC model has a structure similar to the Wiener system. From this Wiener perspective, this work builds a parameter identification approach for this model upon the well-known maximum (MAP) estimation. The purpose of using MAP is to overcome the nonconvexity and local minima that can cause unphysical parameter estimates. A quasi-Newton-based method is developed to accomplish the involved optimization procedure numerically. The proposed approach is the first one that we aware of exploits MAP for Wiener system identification. It also demonstrates significant effectiveness for accurate identification of the NDC model as validated through experiments.

Keywords: Identification, Model Validation, Modeling
Abstract: A methodology for parameterizing polymer electrolyte membrane (PEM) fuel cell models is presented. The procedure starts by optimal experimental design (OED) for parameter identification. This is done by exploring output sensitivities to parameter variations in the space of operating conditions. Once the optimal operating conditions are determined, they are used to gather synthetic experimental data. The synthetic data are then used to identify 7 model parameters in a step-by-step procedure that involves grouping the parameters for identification based on the preceding sensitivity analysis. Starting from the kinetic region of the polarization curve and continuing with the ohmic and mass transport regions, the parameters are identified in a cumulative fashion using a gradient-based nonlinear least squares algorithm. The impact of the OED for parameter identification is explored by comparing the results with another set of synthetic data obtained by Latin Hypercube Sampling (LHS) of the operating space. The results indicate improved identification with OED compared to LHS and point to the utility of the systematic approach, presented herein, for parameter identification for PEM fuel cell models.

Keywords: Automotive systems, Observers for nonlinear systems, Hierarchical control
Abstract: Complex power systems have dynamics spanning multiple energy domains and operating at multiple time scales. Hierarchical control has been proven to guarantee successful management of the coupling between the resulting fast transients and slow dynamics. It is usually prohibitively expensive or even infeasible to measure every signal in the system. Therefore, a reliable estimation framework that provides accurate estimates is vital to the success of the control design. This paper proposes a multi-level hierarchical estimation approach that can be used to supply reliable estimates to hierarchical controllers of complex multi-domain power systems. Models of complex multi-domain power systems can be accurately represented using graphs. System decomposition can be then achieved using clustering algorithms from graph theory. In this work, local estimates at each level of the hierarchical estimator are obtained using extended Kalman filters. A hierarchical estimator-controller is designed for an automotive electric vehicle as an illustrative example.

Keywords: Learning, Machine learning, Stochastic systems
Abstract: Learning theory has been studied for a long time by philosophers, and in the last century by psychologists and engineers. Yet, all learning is carried out in a general deterministic or stochastic environment, mostly by one isolated learner. This paper discusses the concept of mutual learning, where two or more entities attempt to learn from each other. The question posed is: “If two or more entities are learning in the same or similar environments trying to solve the same or similar tasks, how can they share their learning to improve themselves?” The authors believe that this is a central question that will keep researchers busy for many years. The paper merely introduces this question for discussion, and suggests some preliminary answers using the well known stochastic learning automaton framework for reinforcement learning.

Keywords: Learning, Modeling, Optimization
Abstract: Complete models of physical systems enable a plethora of model-based methods in control, diagnosis or prognosis. Proprietary information and system complexity often hinder building such models. We address here the problem of learning physical representations of components in partially known physical systems. These representations need to be feasible: when included in the system model, at minimum the model has to simulate. We propose mathematical models for the component representations and give necessary and sufficient conditions for their feasibility.We demonstrate our approach on a illustrative example where we learn different representations of an unknown resistor component in an electrical circuit.

Keywords: Learning, Predictive control for nonlinear systems, Adaptive control
Abstract: A hybrid control architecture for nonlinear dynamical systems is described which combines the advantages of model based control with those of real-time learning. The basic idea is to generate input-output data from an error system involving the plant and a proposed model. A discretized Chen-Fliess functional series is then identified from this data and used in conjunction with the model for predictive control. This method builds on the authors' previous work on model-free control of a single-input, single-output Lotka-Volterra system. The problem is revisited here, but now with the introduction of a model for the dynamics. The single-input, multiple-output version of the problem is also investigated as a way to enhance closed-loop performance.

Keywords: Constrained control, Learning, Adaptive control
Abstract: Constraints, including thermal, power, traction and rollover limits, as well as actuator range and rate limits, are ubiquitous in advanced ground vehicles and propulsion systems, and in their components, especially as these systems are downsized. These vehicles and systems will be operating in unknown environments where the recognition and avoidance of degradation or damage will be required. This paper proposes a model-free learning algorithm that over time modifies the parameters of an explicit reference governor (ERG) scheme so that violations of pre-specified constraints are avoided after a sufficiently informative learning phase. The ERG modifies set-point commands to a nominal closed-loop system. Our learning algorithm modifies the ERG parameters based on observed constraint violations during a learning phase so as to eliminate constraint violations after learning is completed. Theoretical properties of the algorithm are analyzed and several examples that illustrate its effectiveness are presented.

Keywords: Fluid flow systems, Estimation, Adaptive systems
Abstract: We present a learning-based robust observer design for thermal-fluid systems, pursuing an application to efficient energy management in buildings. The model is originally described by Boussinesq equations which is given by two partial differential equations (PDEs) on velocity field and temperature profile under incompressible flow. Using proper orthogonal decomposition (POD), the PDEs are reduced to a set of nonlinear ordinary differential equations (ODEs). Given a set of temperature and velocity point measurements, a nonlinear state observer is designed to reconstruct the entire state under the error of initial states, and model parametric uncertainties. We prove that the closed loop system of observer error state satisfies an estimate of L2 norm in a sense of locally input-to-state stability (LISS) with respect to parameter uncertainties. Moreover, the system uncertain parameters estimate used in the designed observer are optimized through iterations using a data-driven extremum seeking (ES) algorithm. Numerical simulation on a 2D Boussinesq PDE illustrates the performance of the proposed adaptive estimation method.

Keywords: Mechatronics, Learning, Adaptive systems
Abstract: Feedforward control is essential in high-performance motion control. The aim of this paper is to develop a unified framework for automatic feedforward optimization from both batch-wise data sets as well as real-time data. A statistical analysis is employed to analyze the effect of noise, i.e., an iteration varying disturbance, on feedforward controller performance. This provides new insights, both potential advantages as well as possible hazards of real-time estimation are considered. Finally, a case study confirms and illustrates the results.

Keywords: Automotive systems, Sensor fusion, Multivehicle systems
Abstract: This paper explores the challenges in developing an inexpensive on-bicycle sensing system to track vehicles at a traffic intersection. In particular, opposing traffic with vehicles that can travel straight or turn left are considered. The estimated vehicle trajectories can be used for collision prevention between bicycles and left-turning vehicles. A compact solid-state 2-D low-density Lidar is mounted at the front of a bicycle to obtain distance measurements from vehicles. Vehicle tracking can be achieved by clustering based approaches for assigning measurement points to individual vehicles, introducing a correction term for position measurement refinement, and by exploiting data association and interacting multiple model Kalman filtering approaches for multi-target tracking. The tracking performance of the developed system is evaluated by both simulation and experimental results. Two types of scenarios that involve straight driving and left turning vehicles are considered. Experimental results show that the developed system can successfully track cars in these scenarios accurately in spite of the low measurement density of the sensor.

Keywords: Optimal control, Automotive control
Abstract: In this paper, the pulse and glide (PnG) operation for parallel hybrid electric vehicles (HEVs) with step-gear transmission in cruising scenario is considered. The PnG operation is an eco-driving technique that alternately turns on and off the engine to achieve better fuel economy. For HEVs, the batteries can serve as yet another energy buffer in addition to the vehicle body. However, due to the power loss during battery charging and discharging, the fuel saving performance of PnG using only battery is much degraded, even though it can lead to better ride comfort. Therefore, in this work, we introduce speed oscillation to SOC-PnG that originally only oscillates the state-of-charge (SOC) of the battery. Meanwhile, desired ride comfort and SOC sustenance requirements need to be fulfilled. To achieve this goal, two optimal control problems are formulated, one for the gliding phase and the other for the pulsing phase. By linearizing the vehicle dynamics and applying the McCommick envelop approach, the problems can be transcribed into quadratic programming (QP) problems using the Legendre pseudo-spectral (PS) method. The resultant QP problems can be solved efficiently. Therefore, this framework is suitable for real-time on-line implementation. Numerical simulations are conducted to show the ability of the proposed method to improve fuel economy, while maintaining desired ride comfort and sustaining the battery SOC.

Keywords: Automotive control, Automotive systems, Autonomous systems
Abstract: The vehicle steering-control behavior is highly dependent on the road surface. However, the road surface conditions are typically unknown a priori, and control actions that are safe to perform on asphalt may therefore lead to vehicle instability on low-friction surfaces. It is therefore important that the road surface is estimated, or at least detected, online, and that the vehicle dynamics control algorithms are adapted to the changing conditions. In this paper, we propose a nonlinear model-predictive control (NMPC) scheme that adapts its tire parameters in response to the estimated road surface. We show how estimating the initial slope of the tire-force curve can be used to change the full nonlinear tire-curve used by the NMPC and validate the method in simulation.

Keywords: Automotive systems, Automotive control
Abstract: In electric vehicles, the propulsion configuration with the driving motor installed inside or close to the wheel has been treated as an option. Despite the advantages of the in-wheel drive system, negative effects brought by the increased unsprung mass, e.g. wheel and body vibrations, should be examined. In this work, we investigated this problem based on a proposed coordinated vehicle dynamics control system considering the desired path following as well as the body motion. The desired vehicle motion is determined by the generalized forces/moment, generated by a high-level MIMO sliding mode controller (SMC). Based on the tire normal force allocation, the generalized lateral, longitudinal forces, and yaw moment were distributed to the slip and slip angel of each tire by a fixed-point control allocation algorithm. With respect to the uneven road, a normal force robust tracking controller was proposed as a part of the low-level control. Evaluation of the unsprung mass effect was accomplished by simulations with a full-vehicle CarSim model under a fast double lane-changing maneuver. Comparison results with different unsprung mass configurations and road conditions reveal how the increased unsprung mass affects the vehicle dynamic performance in terms of vehicle planar and spatial motion.

Keywords: Automotive systems, Modeling, Stochastic systems
Abstract: Entrances into highway represent critical sections, as entering vehicles are bound to merge with the existing traffic at a fixed junction point almost independently from the traffic conditions - in some sense a "constrained" cut-in manoeuver. Advanced driver - assistance systems (ADAS) need to perform correctly under these conditions as well, but could also be used to facilitate merging and reducing risks. In particular, vehicles on the main road could adapt their speeds - or even change lane - provided an estimate of the entering vehicle's time to merge is available, exactly as a human driver would do. This paper is concerned with providing such an estimate. To this end, we observe that the speed profile on the entering ramps is rather well predictable if the acceleration of the entering vehicles is described as a function of their actual distance from the junction point. Still, uncertainties remain and to cope with them, we use a stochastic prediction model based on Dynamic Bayesian Network. The result are probability distribution functions of the time to merge based on the observation of speed and distance of the entering vehicle once they become visible to the traffic on the main road. Experimental data are used to illustrate the model structure and the parameter determination. The model quality is then assessed by comparing statistics from simulations with the one recorded on road. The possible use of the model as a tool for traffic prediction algorithm embedded in ADAS and an extension to existing highway stochastic traffic models are shortly discussed at the end of the paper.

Keywords: Automotive control, Constrained control, Stability of nonlinear systems
Abstract: How to guarantee the safety of autonomous ground vehicles is still a significant challenge, although different active safety control systems have been developed, including techniques based on controlled invariant sets and control barrier functions (CBF) that have been proven to guarantee the safety of dynamic systems. To apply these techniques to vehicle safety control, we regarded an estimated lateral stability region of a vehicle as a controlled invariant set. However, the stability region was found to vary with respect to the system control input, which is normally the steering angle. Therefore, the existing definition of the controlled invariant set, which is independent of control inputs, may not be applicable. In this paper, the definition of a controlled invariant set is extended to a control-dependent invariant set, so that the controlled invariant set can vary with control inputs. Based on the extended definition, a new invariance condition using control-dependent barrier function (CDBF) is proposed and applied to the guaranteed vehicle safety control problem. Finally, the guaranteed vehicle safety control is demonstrated by simulation results.

Keywords: Predictive control for nonlinear systems, Chemical process control, Lyapunov methods
Abstract: Control Lyapunov-Barrier function (CLBF) has been used to design controllers for nonlinear systems subject to input constraints to ensure closed-loop stability and process operational safety simultaneously. In this work, we developed Control Lyapunov-Barrier functions for two types of unsafe regions (i.e., bounded and unbounded sets) to solve the problem of stabilization of nonlinear systems with guaranteed safety. Specifically, discontinuous control actions are required to handle potential stationary points (except the origin) for Control Lyapunov-Barrier function in the presence of a bounded unsafe region embedded within the closed-loop system stability region. Subsequently, the CLBF-MPC is developed by incorporating CLBF-based constraints to guarantee closed-loop stability with safety and also ensure the avoidance of convergence to other stationary points if there is a bounded unsafe region. In the case of unbounded unsafe sets, closed-loop stability with safety is readily guaranteed under the CLBF-MPC since the origin is the unique stationary point in state-space. The application of the proposed CLBF-MPC method is demonstrated through a chemical process example with a bounded and an unbounded unsafe region, respectively.

Keywords: Predictive control for nonlinear systems, Chemical process control, Simulation
Abstract: This work demonstrates an application of Safeness Index-based model predictive control to improve process operational safety in a safety critical chemical process application. Specifically, a high-pressure flash drum separator together with pressure relief valve as safety system is used to analyze the benefits of integrating Safeness Index-based considerations in model predictive control (MPC). Safeness Index function and Safeness Index threshold are developed using information collected from the process and safety system to indicate the safeness of the plant. Under an identified linear model, MPC is implemented with Safeness Index-based constraints and slack variables in a co-simulation of Matlab/Aspen. It is demonstrated that in the presence of a small disturbance, the drum pressure remains below the opening pressure of relief valve by Safeness Index-based MPC, so that the safety system is not activated. When there is a large disturbance, the controller working together with the relief valve ensures process operational safety before, during and after the pressure relief valve is turned on/off.

Keywords: Predictive control for nonlinear systems, Linear parameter-varying systems, Constrained control
Abstract: This paper present a Nonlinear Model Predictive Control (NMPC) scheme for systems in input-output quasi-Linear Parameter Varying (IO-qLPV) form. Stability is guaranteed by the use of terminal ingredients: a terminal cost associated with a control Lyapunov function, with a dual mode controller within a terminal constraint set. These terminal ingredients are computed offline by solving an LMI problem, using a recent result from IO-LPV literature. Online computational complexity is kept low by solving the nonlinear optimization problem as a sequence of Quadratic Programs. A simulation example on a 2-DOF robotic manipulator illustrates the approach and the computation of the terminal ingredients.

Keywords: Predictive control for nonlinear systems, Mechatronics, Feedback linearization
Abstract: This work addresses the advantages of using a CTC (Computed-Torque Control) law within a NMPC (Nonlinear Model Predictive Control) scheme. More precisely, the CTC law leads to stable linear closed-loop dynamics after the end of the prediction horizon. By choosing appropriate control gains, a positive invariant ellipsoidal set in which the input constraints are satisfied is determined. Using this set as terminal region in the NMPC problem, together with additional assumptions provides recursive feasibility and asymptotic stability guarantees. This proposed approach can be applied for any robotic manipulators admitting a CTC law, as a particular example. To prove its benefits some simulation results and comparisons with quasi-infinite horizon NMPC over the classical inverted pendulum dynamics are presented.

Keywords: Predictive control for nonlinear systems, Nonlinear systems identification
Abstract: This paper revisits the problem of nonlinear model predictive control (NMPC) using a second order Volterra-Laguerre model structure. While previous NMPC results used a simplified version of the structure with only one set of Laguerre basis functions, recent advances in regularized Volterra series identification allow direct estimation of a more complex Volterra-Laguerre structure. To accommodate this, we develop a more flexible state space representation of the second order model, and derive the corresponding NMPC optimization problem and its analytic solution. The proposed approach is compared with previous methods on a simulation example to demonstrate the performance benefits.

Keywords: Predictive control for nonlinear systems, Optimal control, Process Control
Abstract: The paper deals with systems which are optimally operated when the system states remain in a certain subset of the state space. We define this mode of optimal operation as optimal set operation. We characterize optimal set operation by a dissipativity condition with a parametric storage function. Both, the definition and the dissipativity condition for optimal set operation, constitute a eneralization of the definition and the dissipativity condition for the well-studied scenarios of optimal steady-state or optimal periodic operation. Furthermore, we present an economic MPC scheme with optimized terminal equality constraint that yields convergence of the MPC closed-loop system to the set where the system is optimally operated.

Keywords: Formal verification/synthesis, Networked control systems, Smart grid
Abstract: This paper presents an assume-guarantee reasoning approach to the computation of robust invariant sets for network systems. Parameterized signal temporal logic (pSTL) is used to formally describe the behaviors of the subsystems, which we use as the template for the contract. We show that set invariance can be proved with a valid assume-guarantee contract by reasoning about individual subsystems. If a valid assume-guarantee contract with monotonic pSTL template is known, it can be further refined by value iteration. When such a contract is not known, an epigraph method is proposed to solve for a contract that is valid, ---an approach that has linear complexity for a sparse network. A microgrid example is used to demonstrate the proposed method. The simulation result shows that together with control barrier functions, the states of all the subsystems can be bounded inside the individual robust invariant sets.

Keywords: Autonomous systems, Multivehicle systems, Aerospace
Abstract: This paper investigates a problem of defending safety-critical infrastructure from an adversarial aerial attacker in an urban environment. A circular arc formation of defenders is formed around the attacker, and vector-field based guidance laws herd the attacker to a predefined safe area in the presence of rectangular obstacles. The defenders' formation is defined based on a novel vector field that imposes super-elliptic contours around the obstacles, to closely resemble their rectangular shape. A novel finite-time stabilizing controller is proposed to guide the defenders to their desired formation, while avoiding obstacles and inter-agent collisions. The efficacy of the approach is demonstrated via simulation results.

Keywords: Network analysis and control, Distributed control, Smart grid
Abstract: Inverter-interfaced microgrids differ from the traditional power systems due to their lack of inertia. Vanishing timescale separation between voltage and frequency dynamics makes it critical that faster-timescale stabilizing control laws also guarantee by-construction the satisfaction of voltage limits during transients. In this article, we apply a barrier functions method to compute distributed active and reactive power setpoint control laws that certify satisfaction of voltage limits during transients. Using sum-of-squares optimization tools, we propose an algorithmic construction of these control laws. Numerical simulations are provided to illustrate the proposed method.

Keywords: Formal verification/synthesis, Autonomous systems
Abstract: In this paper, we propose a general approach to derive runtime enforcement implementations for multi- agent systems, called shields, from temporal logical specifications. Each agent of the multi-agent system is monitored, and if needed corrected, by the shield, such that a global specification is always satisfied. The different ways of how a shield can interfere with each agent in the system in case of an error introduces the need for quantitative objectives. This work is the first to discuss the shield synthesis problem with quantitative objectives. We provide several cost functions that are utilized in the multi-agent setting and provide methods for the synthesis of cost-optimal shields and fair shields, under the given assumptions on the multi-agent system. We demonstrate the applicability of our approach via a detailed case study on UAV mission planning for warehouse logistics and simulating the shielded multi-agent system on ROS/Gazebo.

Keywords: Formal verification/synthesis, Hybrid systems, Power electronics
Abstract: In this paper, we illustrate a new correct-by-construction switching controller for a power converter with event-triggered measurements. The event-triggered measurement scheme is beneficial for high frequency power converters because it requires relatively low-speed sampling hardware and is immune to unmodeled switching transients. While providing guarantees on the closed-loop system behavior is crucial in this application, off-the-shelf abstraction-based techniques cannot be directly employed to synthesize a controller in this setting because controller cannot always get instantaneous access to the current state. As a result, the switching action has to be based on slightly out-of-date measurements. To tackle this challenge, we introduce the out-of-date measurement as an extra state variable and project out the inaccessible real state to construct a belief space abstraction. The properties preserved by this belief space abstraction are analyzed. Finally, an abstraction-based synthesis method is applied to this abstraction. We demonstrate the controller on a constant on-time buck voltage regulator plant and a event-triggered sampler. The simulation verifies the effectiveness of our controller.

Keywords: Uncertain systems, Optimal control, Aerospace
Abstract: This paper introduces a strategy for satisfying basic control objectives for systems whose dynamics are almost entirely unknown. This setting is motivated by a scenario where a system undergoes a critical failure, thus significantly changing its dynamics. In such a case, retaining the ability to satisfy basic control objectives such as reach-avoid is imperative. To deal with significant restrictions on our knowledge of system dynamics, we develop a theory of myopic control. The primary goal of myopic control is to, at any given time, optimize the current direction of the system trajectory, given solely the limited information obtained about the system until that time. Building upon this notion, we propose a control algorithm which simultaneously uses small perturbations in the control effort to learn local system dynamics while moving in the direction which seems to be optimal based on previously obtained knowledge. We show that the algorithm results in a trajectory that is nearly optimal in the myopic sense, i.e., it is moving in a direction that seems to be nearly the best at the given time, and provide formal bounds for suboptimality. We demonstrate the usefulness of the proposed algorithm on a high-fidelity simulation of a damaged Boeing 747 seeking to remain in level flight.

Keywords: Adaptive control, Adaptive systems, LMIs
Abstract: In model reference adaptive control of uncertain dynamical systems, the presence of actuator and unmodeled dynamics are unavoidable. Our previous work focused on an adaptive control architecture for this problem and showed two sufficient stability conditions. In particular, the first condition captured the tradeoff between system uncertainties and actuator dynamics (related to the quadratic stability of a matrix) and the second condition captured the effect of unmodeled dynamics (related to the positive-definiteness of another matrix). This paper now proposes a robustifying term in this adaptive control architecture. Specifically, we show that this term results in an added nonnegative matrix in the second stability condition, where this can allow for a relaxed stability condition. Numerical examples are provided to demonstrate the efficacy of the proposed robustifying term on a coupled mechanical system.

Keywords: Adaptive control, Machine learning, Switched systems
Abstract: Controlling hybrid systems - a system that exhibits continuous and discrete behavior simultaneously - is of great interest since the new millennium. Switched linear systems are especially interesting due to the large amount of applications that may be solved. However, applying different control schemes on switched systems entails difficulties in identifying the underlying models and the transitions that occur between them.

In this paper an automatic identification and switching for Multi-Model Reference Adaptive Control (MMRAC) scheme is proposed. The identification of the submodels is performed by curve clustering of the states plotted in the phase portrait. An unsupervised learning algorithm is proposed to cluster the curves. Each curve represents a single submodel and is paired with an MRAC. After the clustering process, correlation between every submodel and the current state is checked. Then the MRAC paired with the best representing curve is used to control the plant, and update the parameters of the curve and the MRAC itself.

The results of two simulations are presented in the end of this paper.

Keywords: Control system architecture, Constrained control, Adaptive control
Abstract: By introducing a system error-dependent learning rate, the recently proposed set-theoretic model reference adaptive control architecture provides user-defined worst-case performance guarantees on the system error between an uncertain dynamical system of interest and a given reference model. In this architecture, the adaptation process is always active. However, it is of practical interest to stop the adaptation process when it is not needed (i.e., in the presence of small system errors). Motivated from this standpoint, we present a new set-theoretic model reference adaptive control architecture with dead-zone effect. The key feature of our framework utilizes a modified and continuous generalized restricted potential function in the adaptation update law, where it not only stops the adaptation process inside the dead-zone (i.e., when the system error is small) but also allows the norm of the system error to be less than a-priori, user-defined worst-case performance bound. The efficacy of the proposed architecture is also demonstrated through an illustrative numerical example.

Keywords: Adaptive control
Abstract: For a general multi-input multi-output linear time-invariant system with unknown parameters, a multivariable model reference adaptive control (MRAC) design guarantees asymptotic output tracking, shown by a sophisticated analysis in the literature. This paper further shows a stronger higher-order convergence property for the signal components of the tracking error. It is proved that under the same MRAC design conditions, not only a tracking error component but its higher-order time-derivatives converge to zero, with the order related to system's infinite zero structure characterized by the system modified interactor matrix. Both cases of a diagonal modified interactor matrix and a non-diagonal modified interactor matrix are studied in the paper, and the new MRAC tracking property is proved for different forms of the modified interactor matrix.

Keywords: Direct adaptive control, Time-varying systems
Abstract: An extension of model reference adaptive control is presented that accommodates use of a time-varying reference model. Specifically, the reference model is taken to be a time-varying convex combination of two linear, time-invariant models. The design is intended to act as a way to smoothly transition between two different reference models without resorting to a scheduled switch. It also provides the ability to use an interpolated reference model when the plant is operating between design points. The time variation of the combination must satisfy some requirements to ensure stability but is otherwise user choice. Subject to these requirements, bounded tracking error behavior is demonstrated via Lyapunov stability analysis for the single-input, single-output, output feedback case. Tracking error convergence is asymptotic when time variation ceases. The proposed design is demonstrated in simulation of a numerical model.

Keywords: Observers for nonlinear systems, Nonlinear output feedback, Uncertain systems
Abstract: This paper addresses the state estimation and control of a robot/prosthesis control system with four joints: vertical hip displacement, thigh, knee and ankle angles. The motivation was inspired by several drawbacks regarding the usage of load cells and/or sensors in robots and prosthetic legs to capture gait data, external forces (GRFs) and moments during walking. We propose the implementation of a high gain observer (HGO) to estimate the prosthesis joint velocities with a time-varying HGO gain synthesized from measurable signals designed to reduce the amount of noise in the control effort while keeping an acceptable tracking error transient performance. Numerical simulations analyze the robustness of the closed-loop control with respect to parametric errors and measurement noise.

Keywords: Observers for nonlinear systems, Robotics, Spacecraft control
Abstract: This paper presents a novel nonlinear pose filter evolved directly on the Special Euclidean Group SE(3) with guaranteed characteristics of transient and steady-state performance. The above-mention characteristics can be achieved by trapping the position error and the error of the normalized Euclidean distance of the attitude in a given large set and guiding them to converge systematically to a small given set. The error vector is proven to approach the origin asymptotically from almost any initial condition. The proposed filter is able to provide a reliable pose estimate with remarkable convergence properties such that it can be fitted with measurements obtained from low-cost measurement units. Simulation results demonstrate high convergence capabilities and robustness considering large error in initialization and high level of uncertainties in measurements.

Keywords: Observers for nonlinear systems, Stability of nonlinear systems
Abstract: In this paper, we design a nonlinear observer to estimate the pose and the velocity of a free-floating non-cooperative target using only pose measurements. In the context of control design for orbital robotic capture of such a non-cooperative target, due to lack of navigational aids, only a pose estimate may be obtained from slow-sampled and noisy exteroceptive sensors. The velocity, however, cannot be measured directly. To address this problem, we develop a model-based observer which acts as an internal model for target kinematics/dynamics and therefore, may act as a predictor during periods of no measurement. To this end, firstly, we formalize the estimation problem on the SE(3) Lie group with different state and measurement spaces. Secondly, we develop the kinematics and dynamics observer such that the overall observer error dynamics possesses a stability property. Finally, the proposed observer is validated through robust Monte-Carlo simulations and experiments on a robotic facility.

Keywords: Observers for nonlinear systems, Vision-based control, Autonomous systems
Abstract: This paper develops observers and controllers for relative estimation and circumnavigation of a moving ground target using bearing-only measurements or range with bearing measurements. A bearing-only observer, range with bearing observer, a general circumnavigation velocity command for an arbitrary aircraft, and nonlinear velocity-based multirotor controller are developed. The observers are designed in the body-fixed reference frame, while the velocity command and multirotor controller are developed in the body-level frame, independent of aircraft heading. This enables target circumnavigation in GPS-denied environments when only a camera-IMU estimator is used for state estimation and ensures observable conditions for the estimator. Simulation results demonstrate the effectiveness of the observers, velocity command, and multirotor controller under various target motions.

Keywords: Observers for nonlinear systems
Abstract: In our previous work, we developed a multi-rate sampled-data observer design method in nonlinear systems with asynchronous sampling. In this article, possible measurement delays are accounted for in the multi-rate observer design. The proposed observer adopts an available multi-rate design in the time interval between two consecutive delayed measurements. A dead time compensation approach is developed to compensate for the effect of delay and update past estimates when a delayed measurement arrives. It is shown that stability of the multi-rate observer is preserved under non-constant, arbitrarily large delays, in the absence of measurement errors. The proposed multi-rate multi-delay observer is applied to a gas-phase polyethylene reactor example and provides reliable estimates in the presence of nonuniform sampling and non-constant delays.

Keywords: Nonlinear output feedback, Time-varying systems, Observers for nonlinear systems
Abstract: We use finite time reduced order continuous-discrete observers to solve an output feedback stabilization problem for a class of nonlinear time-varying systems whose outputs contain uncertainty. Unlike earlier works, our feedback is discontinuous, but it does not contain distributed control terms. Our trajectory based approach is based on a contractivity condition. We illustrate our control in the context of a tracking problem for nonholonomic systems in chained form.

Keywords: Robust control, Stability of linear systems, Linear parameter-varying systems
Abstract: This paper concerns the robust stabilization of continuous-time polytopic systems subject to unknown but bounded perturbations. To tackle this problem, the attractive ellipsoid method (AEM) is employed. The AEM aims to determine an asymptotically attractive (invariant) ellipsoid such that the state trajectories of the system converge to a small neighborhood of the origin despite the presence of non-vanishing perturbations. An alternative form of the elimination lemma is used to derive new LMI conditions, where the state-space matrices are decoupled from the stabilizing Lyapunov matrix. Then a robust state-feedback control law is obtained by semi-definite convex optimization, which is numerically tractable. Further, the gain-scheduled state-feedback control problem is considered within the AEM framework. Numerical examples are given to illustrate the proposed AEM and its improvements over previous works. Precisely, it is demonstrated that the minimal size ellipsoids obtained by the proposed AEM are smaller compared to previous works, and thus the proposed control design is less conservative.

Keywords: Robust control, Optimal control, Spacecraft control
Abstract: This paper presents a computationally efficient robust model predictive control law for discrete linear time invariant systems subject to additive disturbances that may depend on the state and/or input norms. Despite the dependency being non-convex, we are able to handle it as a second-order cone program. Both open-loop and semi-feedback planning strategies are presented. The formulation has linear complexity in the planning horizon length. The approach is thus amenable to efficient real-time implementation with a guarantee on recursive feasibility and global optimality. Robust position control of a satellite is considered as an illustrative example.

Keywords: Robust control, Predictive control for linear systems, Autonomous systems
Abstract: This paper deals with the problem of time-constrained navigation of a robot modeled by uncertain nonlinear non-affine dynamics in a bounded workspace of mathbb{R}^n. Initially, we provide a novel class of robust feedback controllers that drive the robot between Regions of Interest (RoI) of the workspace. The control laws consists of two parts: an on-line controller which is the outcome of a Finite Horizon Optimal Control Problem (FHOCP); and a backstepping feedback law which is tuned off-line and guarantees that the real trajectory always remains in a bounded hyper-tube centered along the nominal trajectory of the robot. The proposed controller falls within the so-called tube-based Nonlinear Model Predictive control (NMPC) methodology. Then, given a desired high-level specification for the robot in Metric Interval Temporal Logic (MITL), by utilizing the aforementioned controllers, a framework that provably guarantees the satisfaction of the formula is provided. The proposed framework can handle the rich expressiveness of MITL in both safety and reachability specifications. Finally, the proposed framework is validated by numerical simulations.

Keywords: Robust control, Optimization, Optimization algorithms
Abstract: Nonlinear disturbance observers owing to their simplicity in implementation have seen remarkable success in motion control. Although, the underlying principle behind disturbance observers is straightforward, finding the corresponding observer gain matrix is non-trivial and often becomes a problem specific task. In addition, for cases where the disturbance enters the system through a different channel than the input, termed as the mismatching condition, disturbance observer and compensation design can be challenging. In this paper, we propose a sum-of-squares method to design disturbance observers for polynomial nonlinear systems with guaranteed exponential stability. The exponential stability of the disturbance observer enables its usage in a composite control structure with any asymptotic or finite time stable control scheme. Furthermore, we demonstrate the design of the disturbance compensation gain which when applied to systems with mismatched uncertainties ensures input-to-state stability (ISS) of the system. The ISS-Lyapunov analysis for the combined disturbance observer and the polynomial system is carried out using sum-of-squares programming. To numerically validate our proposed approach, we investigate robust disturbance observer based control for both matched and mismatched disturbances.

Keywords: Robust control, Predictive control for nonlinear systems, Network analysis and control
Abstract: In this work, we consider a min-max economic model predictive control (EMPC) for nonlinear networked control systems (NCSs) subject to external disturbances and transmission delays in both sensor-to-controller and controller-to-actuator channels. The min-max EMPC scheme incorporates the disturbances within the design of the model predictive controller by optimizing the input for the worst case along the predictions, and thus can achieve better performance than the normal EMPC scheme. The semi-feedback min-max optimization algorithm is used to generate the control sequence to compensate for delayed control input. Simulation results of a numerical example and a CSTR process example are provided to demonstrate the applicability and effectiveness of our approach.

Keywords: Robust control, Predictive control for nonlinear systems, Optimal control
Abstract: This paper is concerned with robust, tube-based MPC for control systems with bounded time-varying disturbances. In tube MPC, predicted trajectories are replaced by a robust forward invariant tube (RFIT), a set-valued function enclosing all possible state trajectories under a given feedback control law, regardless of the uncertainty realization. In this paper, the main idea is to characterize RFITs with polytopic cross-sections via a min-max differential inequality for their support functions. This result leads to a conservative but tractable polytopic tube MPC formulation, which can be solved using existing optimal control solvers. The corresponding theoretical developments are illustrated by a numerical case study.

Keywords: Agents-based systems, Fault detection, Estimation
Abstract: In this paper, we investigate state and fault estimation schemes for multi-agent systems. The proposed estimator is based on an ell_1-norm optimization problem, which is inspired by sparse signal recovery in the field of compressive sampling. Moreover, we provide a necessary and sufficient condition such that state and fault signals are correctly estimated. The result presents a fundamental limitation of the algorithm, which shows how many faulty nodes are allowed to ensure a correct estimation. An illustrative example of a vehicle platoon is given to validate the effectiveness of the proposed approach.

Keywords: Agents-based systems, Formal verification/synthesis, Markov processes
Abstract: This paper studies the synthesis of control policies for an agent that has to satisfy a temporal logic specification in a partially observable environment, in the presence of an adversary. The interaction of the agent (defender) with the adversary is modeled as a partially observable stochastic game. The search for policies is limited to over the space of finite state controllers, which leads to a tractable approach to determine policies. The goal is to generate a defender policy to maximize satisfaction of a given temporal logic specification under any adversary policy. We relate the satisfaction of the specification in terms of reaching (a subset of) recurrent states of a Markov chain. We then present a procedure to determine a set of defender and adversary finite state controllers of given sizes that will satisfy the temporal logic specification. We illustrate our approach with an example.

Keywords: Agents-based systems, Game theory, Randomized algorithms
Abstract: In this paper, we study the convex-concave games played by two teams on a network. Each node of the network has local variables from both teams and a local payoff function that is convex in the variables of one team and concave in the variables of the other. The local payoff function may depend on the variables from its neighbors as well. The goal is to find a saddle point of the sum of all local payoff functions. Using the saddle differential operator, we convert the problem to a fixed point problem and propose a synchronous distributed algorithm that can efficiently find the saddle points through a proper splitting of the payoff functions. Its randomized, asynchronous implementation is also discussed. Numerical examples are provided to illustrate the proposed algorithms.

Keywords: Sensor networks, Agents-based systems, Estimation
Abstract: This work presents a distributed algorithm for node localization problems in static sensor networks in n-dimensions. We focus on networks in which n+1 nodes with known locations (anchors) are arbitrarily placed among all other nodes with unknown locations. In the noiseless case, barycentric coordinates computed from range measurements are used to transform the non-convex node localization problem into a standard linear system of equations. Meanwhile, adding independent zero mean Gaussian noise to range measurements turns all barycentric coordinates to dependent random variables with no known standard distribution which may not even be identically distributed. Relying on online optimization methods, we provide a distributed online gradient descent algorithm to solve the noisy range-only localization problem. Finally, comparisons among simple barycentric coordinate averaging, a centralized gradient descent formulation and our distributed algorithm are provided.

Keywords: Agents-based systems, Linear systems
Abstract: Game with linear time-invariant dynamic agents is explored for the case where the payoff function of each agent is concave and continuously differentiable. By using passivation, a control law that asymptotically stabilize the Nash equilibrium is proposed. We use passivation to derive our control laws. We illustrate our approach with a numerical example. We also characterized the class of linear time-invariant system that can be made passive with respect to its output by dynamic feedback.

Keywords: Agents-based systems, Mechanical systems/robotics, Adaptive control
Abstract: In this paper, we mainly focus on developing a new paradigm motivated by investigating the consensus problem of networked Lagrangian systems with unknown time-varying delay and switching topologies. We propose a class of dynamic feedback by adding differentiators to accommodate the discontinuity and uncertainty resulting from time-varying delay and switching topologies, yielding arbitrary times differentiable reference velocities. Using these reference velocities, we develop delay/topology-independent adaptive controllers with arbitrary times differentiable control torques for networked Lagrangian systems. This specific practice motivates the formulation of a new paradigm, referred to as forwardstepping, for handling discontinuity, time-varying uncertainty, and unavailable state measurement in control systems.

Keywords: Markov processes, Formal verification/synthesis, Autonomous systems
Abstract: Active classification, i.e., the sequential decision-making process aimed at data acquisition for classification purposes, arises naturally in many applications, including medical diagnosis, intrusion detection, and object tracking. In this work, we study the problem of actively classifying dynamical systems with a finite set of Markov decision process (MDP) models. We are interested in finding strategies that actively interact with the dynamical system, and observe its reactions so that the true model is determined efficiently with high confidence. To this end, we present a decision-theoretic framework based on partially observable Markov decision processes (POMDPs). The proposed framework relies on assigning a classification belief (a probability distribution) to each candidate MDP model. Given an initial belief, some misclassification probabilities, a cost bound, and a finite time horizon, we design POMDP strategies leading to classification decisions. We present two different approaches to find such strategies. The first approach computes the optimal strategy ``exactly'' using value iteration. To overcome the computational complexity of finding exact solutions, the second approach is based on adaptive sampling to approximate the optimal probability of reaching a classification decision. We illustrate the proposed methodology using two examples from medical diagnosis and intruder detection.

Keywords: Markov processes, Optimization, Estimation
Abstract: In a variety of applications, an agent's success depends on the knowledge that an adversarial observer has or can gather about the agent's decisions. It is therefore desirable for the agent to achieve a task while reducing the ability of an observer to infer the agent's policy. We consider the task of the agent as a reachability problem in a Markov decision process and study the synthesis of policies that minimize the observer's ability to infer the transition probabilities of the agent between the states of the Markov decision process. We introduce a metric that is based on the Fisher information as a proxy for the information leaked to the observer and using this metric formulate a problem that minimizes expected total information subject to the reachability constraint. We proceed to solve the problem using convex optimization methods. To verify the proposed method, we analyze the relationship between the expected total information and the estimation error of the observer, and show that, for a particular class of Markov decision processes, these two values are inversely proportional.

Keywords: Markov processes, Learning, Optimization algorithms
Abstract: In an online Markov decision process (MDP) with time-varying reward functions, a decision maker has to take an action before knowing the current reward function at each time step. This problem has received many research interests because of its wide range of applications. The literature usually focuses on static regret analysis by comparing the total reward of the optimal offline stationary policy and that of the online policies. This paper studies a different measure, dynamic regret, which is the reward difference between the optimal offline (possibly nonstationary) policies and the online policies. The measure suits better the time-varying environment. To obtain a meaningful regret analysis, we introduce a notion of total variation for the time-varying reward functions and bound the dynamic regret using the total variation. We propose an online algorithm, Follow the Weighted Leader (FWL), and prove that its dynamic regret can be upper bounded by the total variation. We also prove a lower bound of dynamic regrets for any online algorithm. The lower bound matches the upper bound of FWL, demonstrating the optimality of the algorithm. Finally, we show via simulation that our algorithm FWL significantly outperforms the existing algorithms in literature.

Keywords: Markov processes, Game theory, Traffic control
Abstract: Markov decision process (MDP) congestion game is an extension of classic congestion games, where a continuous population of selfish agents each solves a Markov decision processes with congestion: the payoff of a strategy decreases as more population uses it. We draw parallels between key concepts from capacitated congestion games and MDPs. In particular, we show that population mass constraints in MDP congestion games are equivalent to imposing tolls/incentives on the reward function, which can be utilized by social planners to achieve auxiliary objectives. We demonstrate such methods in a simulated Seattle ride-share model, where tolls and incentives are enforced for two separate objectives: to guarantee minimum driver density in downtown Seattle, and to shift the game equilibrium towards a maximum social output.

Keywords: Markov processes, Linear systems, LMIs
Abstract: We describe an algorithm for computing the maximal invariant set for a Markov chain with linear safety constraints on the distribution over states. We then propose a Markov chain synthesis method that guarantees finite determination of the maximal invariant set. Although this problem is bilinear in the general case, we are able to optimize the convergence rate to a desirable steady-state distribution over reversible Markov chains by solving a Semidefinite Program (SDP), which promotes efficient computation of the maximal invariant set. We then demonstrate this approach with a decentralized swarm guidance application subject to density upper bounds.

Keywords: Markov processes, Optimal control, Stochastic optimal control
Abstract: In this work, we study dynamic programming (DP) algorithms for partially observable Markov decision processes with jointly continuous and discrete state-spaces. We consider a class of stochastic systems which have coupled discrete and continuous systems, where only the continuous state is observable. Such a family of systems includes many realworld systems, for example, Markovian jump linear systems and physical systems interacting with humans. A finite history of observations is used as a new information state, and the convergence of the corresponding DP algorithms is proved. In particular, we prove that the DP iterations converge to a certain bounded set around an optimal solution. Although deterministic DP algorithms are studied in this paper, it is expected that this fundamental work lays foundations for advanced studies on reinforcement learning algorithms under the same family of systems.

Keywords: Distributed parameter systems, Process Control, Kalman filtering
Abstract: Kuramoto-Sivashinsky partial differential equation (KSE) has attracted a lot of attention from academia and industry due to its ability to describe various physical phenomena associated with both wave and propagation wave front dynamics. This work addresses infinite-dimensional discrete-time Kalman filter design for KSE by applying a state-of-the-art Crank-Nicolson discretization framework which does not account for spatial approximation or order reduction of the underlying model. A novel infinite-dimensional discrete-time Crank-Nicolson discretization is provided and utilized for KSE discretization in time, which is amenable to the ensuing discrete Kalman filter design. In addition, a two-step infinite-dimensional discrete-time Kalman filter is developed for the state estimation of KSE model augmented with the state and measurement noises. Finally, the effectiveness of the presented discrete-time Kalman filter is investigated and validated by simulations.

Keywords: Distributed parameter systems, Process Control, Linear systems
Abstract: This manuscript addresses design of discrete output regulator for an important class of distributed parameter systems problem given by the mono-tubular reactor with recycle stream. The proposed discrete regulator utilizes an output gain based boundary applied feedback to ensure the closed-loop system stability and output tracking of the polynomial type of reference signals. The discrete nature of design is achieved by application of structure preserving Caley-Tustin discretization to the mono-tubular reactor model with recycle given by the first order transport hyperbolic PDE without use of any spatial approximation and/or order reduction. The output regulator design admits the discrete exo-system representation which leads to the corresponding algebraic Sylvester equation which is easily solvable. Finally, the simulation studies provide an insight in the trajectory tracking performances of design controller.

Keywords: Adaptive systems, Distributed parameter systems, Linear systems
Abstract: This paper focuses on rejection of unknown periodic disturbances in a known LTI system with diffusion PDE actuator dynamics by using output feedback. Firstly, the disturbance is parametrized and written as a boundary of heat PDE. Then, the problem is reformulated as an adaptive controller design for an uncertain ODE-PDE couple. Thirdly, an adaptive observer is designed to estimate the ODE system states and unknown disturbance. After that, to cancel the disturbance and compensate the input dynamics, an infinite dimensional backstepping procedure is applied. Finally, an observer based adaptive control algorithm is proposed and stability of the closed loop system equilibrium is confirmed. It is proven that the system states and observer error states converge to zero exponentially. The performance of the controller is validated with a numerical simulation.

Keywords: Adaptive systems, Distributed parameter systems, Linear systems
Abstract: The focus of this paper is cancellation of unknown harmonic disturbances in a known LTI system with diffusion PDE actuator dynamics. Firstly, the disturbance is parametrized and written as a boundary of heat PDE. From there on, the problem can be treated as an adaptive controller design for an uncertain ODE-PDE couple. To cancel the disturbance and compensate the input dynamics, an infinite dimensional backstepping procedure is applied. Finally, an adaptive control algorithm is designed and stability of the closed loop system equilibrium is confirmed. The convergence of the system states to zero as time goes to infinity and the perfect estimation of the disturbance are proven. The performance of the controller is validated with a numerical simulation.