Keywords: Adaptive systems, Biologically-inspired methods, Optimal control
Abstract: This paper proposes a novel optimal adaptive event-triggered control algorithm for nonlinear continuous-time systems. The goal is to reduce the controller updates, by sampling the state only when an event is triggered to maintain stability and optimality. The online algorithm is implemented based on an actor/critic neural network structure. The algorithm proposed exhibits dynamics with continuous evolutions described by ordinary differential equations and instantaneous jumps. A Lyapunov stability proof ensures that the closed-loop system is asymptotically stable.

Keywords: Adaptive systems, Cooperative control
Abstract: An asymptotically stable reference model plays a crucial role in the entire literature on adaptive systems. Given a plant with unknown parameters, the objective is to adapt the parameters of a controller so that the behavior of the controlled plant emulates that of the reference model in some sense. This paper addresses the following question, which is markedly different from that encountered in conventional adaptive control: “Can two or more unstable plants adaptively stabilize each other?”. This is because neither system has a stable model to emulate, and each depends upon the other to stabilize itself. It is not surprising that this seemingly innocuous question has far reaching implications in widely different fields such as biology, psychology, economics and robotics, where it is found to arise frequently. If simple rules of adaptation were adequate to answer the above question, it would not merit much attention. However, preliminary investigations have revealed that such adaptation often does lead to instability. In fact the answer to the question is far from simple, and depends upon the assumptions made regarding the adaptive subsystems, and the manner in which they interact with each other. This has given rise to a vast spectrum of interesting questions. The objective of this paper is consequently not to provide an exhaustive set of answers, but merely to pose several problems in what the authors hope will be a new area of research, and to present preliminary results concerning some of them.

Keywords: Adaptive systems, Identification, Networked control systems
Abstract: Classical schemes in system identification and adaptive control often rely on persistence of excitation to guarantee parameter convergence, which may be difficult to achieve with a single agent and a single input. Inspired by consensus systems, we extend classical parameter adaptation to the multi agent setting by combining an adaptive gradient law with consensus dynamics. The gradient law represents the main learning signal, while consensus dynamics attract each agent's parameter estimates toward those of its neighbors. We show that the resulting decentralized online parameter estimator can be used to identify the true parameters of all agents, even if no single agent employs a persistently exciting input.

Keywords: Adaptive systems, Control applications
Abstract: We investigate a NARMAX controller structure involving hysteretic nonlinearities. A weighted combination of backlash nonlinearities constitutes a Prandtl-Ishlinskii hysteresis model. The rationale for using a Prandtl-Ishlinskii NARMAX (PIN) controller is due to the fact that the describing function of a backlash nonlinearity has both gain and phase shift. By combining backlash nonlinearities into a NARMAX control law it is reasonable to expect that arbitrary gain and phase shift can be attained by adaptively updated controller coefficients.

Keywords: Adaptive systems, Control applications, Nonlinear systems
Abstract: This paper develops an adaptive control system for a quadrotor unmanned aerial vehicle. It employs a state feedback output tracking design for multi-input multi-output systems, using a less restrictive matching condition than a state tracking design, and offers a simpler controller structure than an output feedback design. Some key characteristics of the quadrotor dynamics are derived for adaptive control design which deals with system uncertainties from changing operating points. The plant-model matching is ensured despite of system parameter uncertainties which cannot be handled by an existing state tracking design. The adaptive law is based on a parametrization using an LDS decomposition of the high frequency gain matrix, which ensures closed-loop stability and asymptotic output tracking. A simulation study is carried out on the nonlinear quadrotor model, and results are presented to demonstrate the desired adaptive system performance.

Keywords: Direct adaptive control, Aerospace, Flight control
Abstract: This paper presents the design and simulation of a Modified State Observer (MSO) based adaptive control law, developed by Balakrishnan, for a Micro Aerial Vehicle (MAV). The MSO allows higher adaptive gains than other adaptive control laws. The simulation is carried out using the aerodynamic derivatives of the Black Kite 300 mm wing span MAV developed by CSIR-NAL Bangalore. The controller is tested in simulation for its ability to adapt to modeling errors for the highly responsive MAV. At present, the study is limited to the longitudinal dynamics of the vehicle. Simulation results are presented that show the controller’s ability to respond to altitude and airspeed commands with elevator and engine failures in the presence of parameter uncertainties. The MSO adaptation along with the nonlinear dynamic inversion controller developed using the mathematical model of the MAV, enables the pilot to control the vehicle with a lower workload.

Keywords: Power systems, Control applications, Building and facility automation
Abstract: Recently it has been shown that an aggregation of Thermostatically Controlled Loads (TCLs) can be utilized to provide fast regulating reserve service for power grids and the behavior of the aggregation can be captured by a stochastic battery with dissipation. In this paper, we address two practical issues associated with the proposed battery model. First, we address clustering of a heterogeneous collection and show that by finding the optimal dissipation parameter for a given collection, one can divide these units into few clusters and improve the overall battery model. Second, we analytically characterize the impact of imposing a no-short-cycling requirement on TCLs as constraints on the ramping rate of the regulation signal. We support our theorems by providing simulation results.

Keywords: Power systems, Decentralized control, Agents-based systems
Abstract: A novel decentralized approach to the management of residential distributed energy resources (DER) such as solar PV, wind, fuel cells, energy storage devices, and demand response (DR) is described. This new methodology treats residential electricity customers as peers in a BitTorrent-like power swarm, designed to efficiently dispatch local generation, storage, and demand response capacity to perform real-time demand-side load balancing without centralized control. It is envisioned that the technology could be implemented by utilities as an internet-based service layer that would allow more widespread integration of DER and smart devices within the existing power system. Initial MATLAB simulations of a 10-residence neighborhood with distributed generation (DG) demonstrate that BitTorrent is an effective protocol for decentralized load balancing, significantly reducing both local (peer) and aggregate (swarm) demand spikes.

Keywords: Power systems, Control applications
Abstract: This paper aims to develop a hierarchical framework for demand-side frequency control. The framework involves two decision layers. The top layer determines a control gain for the aggregated load response on each bus using robust decentralized control theory. The second layer involves a large number of devices, which switch probabilistically during contingencies so that the aggregated power change matches the desired droop amount according to the control gains determined in the top layer. The proposed framework is based on the classical nonlinear multi-machine power system model, and can deal with time-varying system operating conditions while respecting the physical constraints of individual devices. Realistic simulation results based on a 68-bus system are provided to demonstrate the effectiveness of the proposed strategy.

Keywords: Power systems, Control applications
Abstract: Leveraging of flexibility in certain types of electric loads such as water heaters, washers, dryers, heating and air conditioning, and electric vehicles, offers an attractive and promising approach for large scale penetration of renewable generation and peak demand reduction. In price/market based approaches, consumers of flexible loads optimize their consumption schedules to meet their energy/power demands while minimizing their total costs. However, it may be that not all consumers will optimize their consumption schedules. We call such consumers ``irrational consumers'' and use the phrase ``rational consumers'' for those who optimize their consumption schedules. In this paper, we analyze the impact of irrational consumers on rational consumers in a stylized supply-demand balancing problem. We derive a condition which characterizes when the optimal utility derived by a rational consumer is lower than the case when all consumers act rationally. This condition uses parameters of the utility functions of consumers and the total consumption of the irrational consumers. The result offers some interesting insights into this phenomenon.

Keywords: Power systems, Building and facility automation
Abstract: Thermostatically Controlled Loads (TCLs) such as air conditioners, water heaters and refrigerators have a great potential for providing regulation reserve to the grid. This paper aims to provide a foundation for a practical method of enabling TCLs to provide regulation service. We study the economic, regulatory, and practical aspects to realize such a vision. We show that the potential of TCLs in California is more than enough for both current and predicted near-future regulation requirements. Moreover, we estimate the cost and revenue of TCLs, discuss the qualification requirements and participation incentive methods, and present a practical control framework for TCLs to provide regulation service. Numerical experiments are also provided to illustrate the efficacy of our methods in addressing practical issues such as short cycling of units, latency in communications, and dynamics modeling errors.

Keywords: Power systems, Building and facility automation, Iterative learning control
Abstract: This paper addresses the optimal power management problems in electric cooling systems based on appropriately constructed thermal dynamic models and cost profiles. In this venue, the dynamics and logical constraints for the cooling load are first formulated as mixed-integer linear programming models. We subsequently apply an online learning algorithm to adjust the weighting factor for customers' satisfaction level considering the fluctuating prices and customers' preferences. The proposed approach is expected to save the user's electricity cost by adequately scheduling the operations of the cooling load without an adverse effect on the entire system. Simulation results for the chiller systems are presented using the proposed approach. The effectiveness of the proposed temperature control and trade-off between electricity cost and customers' satisfaction level is demonstrated via a simulation scenario.

Keywords: Aerospace, Stochastic systems, Predictive control for nonlinear systems
Abstract: In this paper, we apply a receding horizon control approach to the sensor tasking aspect of a simplified version of the Space Situational Awareness (SSA) problem: “Given a small number of sensors and a large number of satellites, how should the sensors be used to maximize the information gained about the states of the satellites” Finding the globally optimal solution to this partially observed Markov decision process is computationally intractable. However, by using a stochastic gradient ascent algorithm proposed in previous work to improve an open-loop control policy over a shortened horizon, large performance improvements can be made over a baseline myopic tasking policy in a computationally tractable manner. The structure of this approach also allows for a distributed implementation in which each sensor acts as an agent that is semi-independent from the others.

Keywords: Aerospace, Vision-based control, Sensor fusion
Abstract: The aiding of an INS using measurements over time of the line of sight of ground features as they come into view of an onboard camera is investigated. The objective is to quantify the reduction in the navigation states’ errors by using bearings-only measurements over time of terrain features in the aircraft’s field of view. INS aiding is achieved through the use of a special Kalman Filter designed for Simultaneous Localization and Mapping (SLAM). The design of the SLAM Kalman Filter is presented and it is shown that during a long range, wings level cruising flight at constant velocity and altitude, a significant reduction in the aided INS-calculated navigation state errors compared to a free INS, is possible.

Keywords: Aerospace, Control applications, Sensor fusion
Abstract: The tailsitter aircraft merges the endurance and speed of fixed-wing aircraft with the flexibility and VTOL abilities of rotorcraft. Typical control and estimation schemes make assumptions about the maximum attitude an aircraft will experience that are not valid for tailsitters. This paper discusses the limitations of a typical EKF magnetometer measurement update that uses Euler angles. It is shown how to use a second set of Euler angles to avoid gimbal lock. A method is given that bypasses the use of Euler angles altogether and directly uses the quaternion to determine heading error and update the attitude estimate. This method highlights the EKF limitations in estimating a quaternion. A multiplicative EKF is briefly explored to overcome these limitations. Hardware results on an actual tailsitter aircraft are presented.

Keywords: Vision-based control, Emerging control theory, Emerging control applications
Abstract: In this work, we present feedback control laws for vision guided navigation of a cart/robot. The cart has two vision sensors onboard. The primary vision sensor is a single-pixel camera that uses CS techniques for capturing images. Additionally, there is a low-resolution sensor that provides coarse side measurements. The novel concept in the paper is that the control laws are computed directly from the compressed measurements. Hence the reconstruction of the measurements is sidestepped leading to a reduction in the amount of data acquired for control.

Keywords: Vision-based control, Optimization algorithms
Abstract: The problem of finding the relative camera pose between two calibrated camera views given five matched feature points is called the “Five Point Relative Pose Problem”. There exists a variety of solutions in the literature. However, existing methods rely on solving an optimization problem that is based on the Essential matrix. The Essential matrix has fundamental weaknesses, and introduces these weaknesses into algorithms that employ it. In this paper, we propose a new and practical method eschews the essential matrix by representing the pose estimation problem in the quaternion space. The new method has numerous advantages. Unlike the Essential Matrix, it is not prone to problems when faced with coplanar points or zero translation between two camera views. Rotation, scaled translation, and scaled depth of the points with respect to both camera frames are simultaneously recovered. Furthermore, the algorithm is robust to noise and can be easily extended to more than five points. Investigations using simulated images under noise have validated the new method and verify that the algorithm can be used in practical context such as Position Based Visual Servoing.

Keywords: Vision-based control, Cooperative control, Autonomous systems
Abstract: This paper investigates coverage control for visual sensor networks based on gradient descent techniques on matrix manifolds. We consider the scenario that networked vision sensors with controllable orientations are distributed over 3 D space to monitor 2-D environment. Then, the decision variable must be constrained on the Lie group SO(3). The contribution of this paper is two folds. The first one is technical, namely we formulate the coverage problem as an optimization problem on SO(3) without introducing local parameterization like Euler angles and directly apply the gradient descent algorithm on the manifold. The second technological contribution is to present not only the coverage control scheme but also the density estimation process including image processing and curve fitting while exemplifying its effectiveness through simulation of moving objects monitoring.

Keywords: Agents-based systems, Robust control
Abstract: Recently, distributed robust output feedback control synthesis has been considered for a subclass of interconnected linear parameter-varying (LPV) systems, referred to as decomposable systems. In the synthesis procedure, the interconnection and the LPV uncertainty are described as linear fractional transformations (LFTs), and conditions derived by the full block S-procedure are solved iteratively. Here different approaches are presented to reduce the size of the LFT channels and thus the number of variables in the synthesis. Furthermore, an alternative synthesis procedure is presented, which in contrast to previously reported results avoids the inversion of multipliers.

Keywords: Reduced order modeling, Automotive, Power systems
Abstract: In this work, we apply the projection-based model-reduction framework for PDEs of [1] to the diffusion process governing the intercalation of lithium ions into spherical particles that appears in electrochemical models of Li-ion batteries. We first invoke the projection framework with different projections (the so-called natural—i.e. Galerkin—and volume-averaging projections—NP and VAP, respectively) and different basis sets (even monomials and eigenfunctions of the PDE) to arrive at different reduced models, which we then benchmark in a dimensionless frequency-domain setting. Surprisingly, we find that VAP of an even monomial basis yields an unstable reduced model for anything but the lowest order; however, NP yields suitable results for the basis sets and orders considered. Furthermore, we find that the NP-eigenfunction combination makes for a slow convergence when compared to NP with even monomials; however, room for further improvement is seen when compared with a balanced truncation, which is not applicable in the nonlinear setting of concentration-dependent diffusivity.

Keywords: Manufacturing systems, Reduced order modeling, Nonlinear systems
Abstract: In this paper, we construct a nonlinear reduced order model of a plasticization cylinder of injection machines,which has a spatially distributed nonlinear dynamics. First, a distributed parameter model for the overall control system is derived based on the physical laws. Next, we attempt to reduce the model complexity focusing on the specific structure of the nonlinearity, which arises from a temperature-dependency of the rate of heat loss of heaters subjected to natural convection. This enables us to obtain a 28 dimensional model (via an 808 dimensional spatially discretized model) while theoretically guaranteeing a practically satisfactory accuracy. The obtained model is examined experimentally by using a prototype system.

Keywords: Reduced order modeling, Nonlinear systems
Abstract: Parameter dependent systems are found in many complex coupled systems. When applying optimization techniques, using full order models for the solution of parameter dependent systems may simply be intractable. Therefore we investigate methods for generating reduced order models that can be applied to nonlinear parameter dependent systems. The development of these reduced order models is made possible by sensitivity analysis of the basis functions. We also introduce a new method using piecewise cubic Hermite interpolating polynomials of the basis functions.

Keywords: Reduced order modeling, Linear systems, Automotive
Abstract: Low order, explicit models of lithium ion cells are critical for real-time battery management system (BMS) applications. This paper presents a linearized 7^th order, electrolyte enhanced single particle model (ESPM) in an explicit impedance transfer function format with electrolyte diffusion effect. The impedance transfer function coefficients are explicit in terms of the model parameters, simplifying the implementation of temperature dependence in the ESPM (ESPM-T). The models are compared with a commercially available finite volume based model and results show accurate matching of pulse responses over a wide range of temperature and C-rates.

Keywords: Reduced order modeling, Large scale systems, Estimation
Abstract: Parameter estimation through reduced-order modeling play a pivotal role in designing real-time optimization schemes for the Oil and Gas upstream sector through the closed-loop reservoir management framework. Reservoir models are in general complex, nonlinear, and large-scale, i.e., large number of states and unknown parameters. Consequently, model reduction problem is of great interest in reducing computational burden in reservoir modeling an simulation. Furthermore, de-correlating system parameters in all history matching and reservoir characterization problems, is an important task due to its effects on reducing ill-posedness of the system. In this paper, we utilize higher order singular value decomposition (HOSVD) to reparameterize reservoir characteristics and perform several forward reservoir simulations by the resulted reduced order map as an input. To acquire statistical consistency we repeat all experiments for a set of 1000 samples using both HOSVD and Proper orthogonal decomposition (POD). In addition, we provide RMSE analysis for a better understanding in process of comparing HOSVD and POD. Results show that HOSVD provide a better performance in a RMSE point of view.

Keywords: Cooperative control, Autonomous systems, Agents-based systems
Abstract: There has been a lot of work in the literature, related to the mapping of boundaries of regions, using multiple agents. Most of these are based on optimization techniques or rely on potential fields to drive the agents towards the boundary and then retain them there while they space out evenly along the perimeter or surface (in two-dimensional and three-dimensional cases, respectively). In this paper an algorithm to track the boundary of a region in space is provided based on the cyclic pursuit scheme. This enables the agents to constantly move along the perimeter in a cluster, thereby tracking a dynamically changing boundary. The trajectories of the agents provide a sketch of the boundary. The use of multiple agents may facilitate minimization of tracking error by providing accurate estimates of points on the boundary, besides providing redundancy. Simulation results are provided to highlight the performance of the proposed scheme.

Keywords: Cooperative control, Agents-based systems, Multivehicle systems
Abstract: This paper presents event-triggered control techniques for the consensus problem with general linear dynamics. A novel consensus protocol is proposed, where each agent implements a model of the decoupled dynamics of its neighbors. We first provide a simple centralized condition to motivate the problem. Then, the focus is placed on designing decentralized consensus protocols. The decentralized approach proposed in this paper not only avoids the need for continuous communication between agents but also provides a decentralized and asynchronous method for transmission of information. This method gives more flexibility for scheduling information broadcasting compared to periodic and sampled-data implementations. Finally conditions are provided in order to guarantee positive inter-event times.

Keywords: Cooperative control, Autonomous systems, (Under)water vehicles
Abstract: We address the synthesis of distributed control policies to enable a homogeneous team of mobile sensing agents to maintain a desired spatial distribution in a geophysical flow environment. Geophysical flows are natural large-scale fluidic environments such as oceans, eddies, jets, and rivers. In this work, we assume the agents have a “map” of the fluidic environment consisting of the locations of the Lagrangian coherent structures (LCS). LCS are time-dependent structures that divide the flow into dynamically distinct regions, and are time-dependent extensions of stable and unstable manifolds. Using this information, we design agent-level hybrid control policies that leverage the surrounding fluid dynamics and inherent environmental noise to enable the team to maintain a desired distribution in the workspace. We validate the proposed control strategy using flow fields given by: 1) an analytical time-varying wind-driven multi-gyre flow model, 2) actual flow data generated using our coherent structure experimental testbed, and 3) ocean data provided by the Navy Coastal Ocean Model (NCOM) database.

Keywords: Cooperative control, Optimization algorithms, Power systems
Abstract: This paper considers the economic dispatch problem for a group of power generating units communicating over an arbitrary strongly connected, weight-balanced digraph. The goal of the group is to collectively meet a specified load while respecting individual generation bounds and minimizing the total generation cost, which corresponds to the sum of individual arbitrary convex functions. We introduce a distributed coordination algorithm, termed Laplacian-set-valued dynamics, and establish its asymptotic convergence to the solutions of the economic dispatch problem. In addition, we show that the algorithm is anytime, meaning that its executions are feasible solutions at all times and the total cost monotonically decreases as time elapses. The technical approach combines notions and tools from algebraic graph theory, nonsmooth analysis, set-valued dynamical systems, and penalty functions. Several simulations illustrate our results.

Keywords: Cooperative control, Control applications, Information theory and control
Abstract: This paper summarizes work that investigates human interactions in terms of communication through motion in cooperative control. We are interested in multi-agent distributed control models with a shared goal and with the agents being required to accomplish secondary objectives. The study presented here extends the earlier work on the analysis of leader-follower interactions in salsa. The move transitions in salsa are described by simple rules suggested by topological knot theory. The initial and final poses of each move in the dance are represented by link diagrams. The link invariant Alexander Polynomial is calculated for classified link diagrams so that the physical motion primitives in dance are described as polynomial function manipulations. The size of the alphabet for the dance moves given in earlier work is extended by introducing three new moves. The leaders' decision process in a transition system is discussed and it is compared with the prior models which had fewer moves. Complexity metrics are proposed to capture the new alphabet and the proficiency hierarchy in the dance.

Keywords: Cooperative control, Agents-based systems, Stability of nonlinear systems
Abstract: This paper proposes a discrete-time distributed algorithm based on a local replicator dynamics that allows a group of nodes to achieve virus mitigation over a connected graph when subject to limited resources. The algorithm is distributed in the sense that it can be implemented by the network nodes via local and anonymous interactions. By employing a discrete-time LaSalle invariance principle, we find a bound on the algorithm step size that guarantees asymptotic convergence for agents subject to time-varying interactions. Several simulations illustrate the algorithm performance.

Keywords: Automotive, Mechatronics, Linear parameter-varying systems
Abstract: Engine electronic throttle control is challenging due to its high nonlinearities. In this paper, a discrete-time electronic throttle system was modeled as an LPV (linear parameter varying) system, where the vehicle battery voltage, the nonlinear friction, and spring are the measurable time-varying parameters. Gain-scheduling H2 controller was designed for the LPV throttle control system using the linear matrix inequality (LMI) convex optimization approach. The designed controller was experimentally validated and compared with the baseline fixed gain PI controller and showed significant performance improvement.

Keywords: Automotive, Predictive control for nonlinear systems, Optimal control
Abstract: The paper presents a Model Predictive Control (MPC) design for a Diesel engine air path system equipped with Variable Geometry Turbine (VGT), Exhaust Gas Recirculation (EGR) and a Variable Geometry Compressor (VGC).

Starting from a validated nonlinear engine model, multiple linearized models were obtained at different operating conditions and a switched linear MPC controller was designed to deal with the nonlinearity of the system. However, such a hard switching controller can generate unexpected outcomes due to the abrupt changes when crossing the switching boundaries.

In order to mitigate the issues related to switching-type controllers, a softly switched mechanism is proposed here, which uses a convex combination of the objective functions during the switching process. A suboptimal solution is then adopted to reduce the computational complexity, allowing one to derive the explicit solution of the finite horizon optimization problem. Finally, simulation studies are conducted, and the results confirm the effectiveness of the proposed methodologies.

Keywords: Automotive, Variable-structure/sliding-mode control, Adaptive systems
Abstract: Automotive powertrain control strategies are a key component of the software design process for vehicle systems. During implementation of control algorithms on real systems, errors often arise that prove costly if they are not detected until the Verification & Validation process. Thus, it is advantageous to mitigate potential uncertainties early on in the design. Specifically, in this paper, we present the control algorithm derivation with the direct incorporation of uncertainty from the system model. We incorporate an unknown parameter representing significant model uncertainty and design to that scenario using a nonlinear, discrete-time sliding control strategy. Additionally, we incorporate an adaptation law to estimate and update the unknown parameter online in order to decrease the control actuation effort. A Simulink representation of the cold start engine emissions process is used as a case study on which the control and adaptation strategy is demonstrated. Simulation results demonstrate that this adaptive formulation yields superior performance over its non-adaptive counterpart by successfully estimating the unknown model parameters and driving tracking error to zero in steady-state.

Keywords: Automotive
Abstract: Dual-loop EGR (exhaust gas recirculation) systems can provide the authorities of controlling the engine intake manifold gas conditions for steady-state and transient operations of advanced combustion modes. Due to the inherent transport delay of an air-path system, the response of the intake manifold oxygen concentration is usually slower than those of the pressure and temperature under conventional control methods. This paper presents a control methodology for dual-loop EGR engine air-path with adjustable intake manifold gas condition priorities, which means that any of the three intake manifold indices can be set with the highest control priority to obtain fast response during transient operations. The reference governor technique and a feedforward control are combined to achieve such a control objective. An acceptable control input set is obtained and the optimal input is chosen from this set according to the priority order. Simulations are conducted on a GT-Power engine model and the results show that the proposed control methodology is valid.

Keywords: Nonlinear systems, Linear parameter-varying systems, Automotive
Abstract: This work investigates the design of nonlinear internal model control (IMC) for wastegate control of a turbocharged gasoline engine. We extend the inverse-based IMC design for linear time-invariant (LTI) systems to nonlinear systems. A fourth-order nonlinear model which sufficiently describes the dynamic behavior of the turbocharged engine is implemented to serve as the model in the IMC structure. To leverage the available tools for LTI IMC deign, we have explored the quasi linear parameter varying (quasi-LPV) model. IMC design through transfer function inverse of the quasi-LPV model is ruled out due to parameter variability. A new approach for nonlinear inverse, referred to as the structured quasi-LPV model inverse, is developed and validated. The controller based on this nonlinear inverse is then designed to achieve boost pressure tracking. Finally, simulations on a validated high fidelity model are carried out to show the feasibility of the IMC. Its closed-loop performance and robustness are compared with a well-tuned PI controller with extensive feedforward and anti-windup built in.

Keywords: Automotive, Mechatronics, Linear parameter-varying systems
Abstract: This paper presents a cycle-based ammonia coverage ratio optimization for Diesel engine two-can selective catalyst reduction (SCR) systems. For a high NOx conversion, it is intuitively required that the ammonia coverage ratio for the first catalyst can is as high as possible. However, a high ammonia coverage ratio would increase the ammonia slip downstream. In order to constrain the ammonia slip downstream, it is expected that the ammonia coverage ratio for the second catalyst can is as low as possible. The low ammonia coverage ratio whereas may not help for the NOx reduction in the second can. Due to the conflict in the ammonia coverage ratios for these two catalyst cans, it is quite interesting and meaningful to study the optimization problem for the ammonia coverage ratios. With the developed system models, we propose a nonlinear model predictive control (NMPC) method to address this optimization problem. Various cases are studies to investigate the effects of the initial values on the ammonia coverage ratios.

Keywords: Constrained control, Automotive, Predictive control for nonlinear systems
Abstract: This paper provides a tutorial overview of reference governors and command governors, which are add-on control schemes for reference supervision and constraint enforcement in closed-loop feedback control systems. The main approaches to the development of such schemes for linear and nonlinear systems are described. The treatment of unmeasured disturbances and parametric uncertainties is addressed. Generalizations to extended command governors, feed-forward reference governors, reduced order reference governors, parameter governors, networked reference governors, decentralized reference governors and virtual state governors are summarized. Examples of applications of these techniques to automotive systems are given. A comprehensive list of references is included. Comments comparing reference and command governor approaches with Model Predictive Control and input shaping, and on future directions in reference and command governor research are included.

Keywords: Identification, Modeling and simulation
Abstract: The capability of ultracapacitors in delivering high power at low temperature applications such as cold starting is the motivation of this paper. A two state equivalent electric circuit model coupled with a two state thermal model is defined and parameterized to develop a four state control oriented electro-thermal model for cylindrical double layer ultracapacitors. The proposed two state equivalent electric circuit model mimics the terminal voltage dynamics and could be used to evaluate the power capability and efficiency of the cell. The electric model parameters are estimated by pulse-relaxation tests for sub-zero temperatures as low as -40 textcelsius. The two state thermal model provides a tool to estimate the surface and core temperature dynamics. The two models are then coupled through the reversible plus irreversible heat generation and also via temperature dependence of the equivalent circuit model parameters. The modeled terminal voltage and surface temperature dynamics are in good agreement with experimental observations for fixed environmental temperature with forced air cooling.

Keywords: Estimation, Observers for nonlinear systems, Control applications
Abstract: State-of-Charge (SOC) information is very crucial for the control, diagnostics and monitoring of Li-ion cells/batteries. Compared to conventional data-driven or equivalent circuit models often employed in battery management systems, electrochemical battery models have the potential to give physically accurate the SOC information by tracking the Li-ion concentration in each electrode. In this paper, two nonlinear observer designs are presented to estimate Li-ion battery State-of-Charge based on reductions of an electrochemical model. The first observer design uses a constant gain Luenberger structure whereas the second one improves it by weighing the gain with the output Jacobian. For both observer designs, Lyapunov’s direct method is applied and the design problems are converted to solving LMIs. Simulation results are included to demonstrate the effectiveness of both observer designs.

Keywords: Estimation, Power systems, Kalman filtering
Abstract: This paper presents new methods for improving state of charge (SOC) estimation accuracy for Lithium Ion battery cells connected in series. The methods benefit from the fact that the cells share a common current trajectory. These methods extend previously studied techniques for SOC estimation, like the Extended Kalman Filter. While the existing literature focuses on estimating SOC for individual cells separately, we consider the cells in a series string collectively. We show that estimation accuracy is increased for cells in series both when they are 1) balanced and 2) un-balanced. We validate these methods against a control case using Monte Carlo simulation.

Keywords: Reduced order modeling, Modeling and simulation
Abstract: This paper evaluates the collective impact of three computational strategies from the literature applied to the Doyle-Fuller-Newman (DFN) lithium-ion battery model, a physics-based model valid for high current rates. The first strategy used is efficient model reformulation, where spatial basis functions are used to represent the distribution of lithium ions and potentials within the battery. The second strategy is quasi-linearization, which is used to lessen the computational burden associated with the nonlinearities of the Butler-Volmer equation. Finally, the combination of the first two strategies furnishes a descriptor-form DAE model of the battery at every integration time step. This paper evaluates the accuracy of these combined methods by evaluating the number of basis functions needed for accurate representation and by evaluating the consistency of the constraint equations when the full model is assembled. The combined methods lead to low computation time with accurate simulations 3-4 times faster than real time on a laptop computer.

Keywords: Modeling and simulation, Identification, Control applications
Abstract: Due to numerous advantages of Polymer Electrolyte Fuel Cells (PEMFCs), they are becoming the mainstream fuel cell of choice for different applications. Open-cathode PEMFCs, in particular, are gaining increased popularity in low to medium power applications due to their simple structure and low parasitic losses. However, in order to achieve safe operation, increased durability and optimal performance, advanced control algorithms, which typically require control-oriented models, need to be implemented on open-cathode fuel cell systems. The open-cathode fuel cell system includes the fuel cell stack and all of the auxiliary components that are vital for its operation. In this paper, control-oriented nonlinear models are developed for individual components of air-forced open-cathode fuel cell systems. The models incorporate electrical, thermal, anode, and cathode system dynamics in addition to the interactions between different subsystems. Specifically, control-oriented purge modeling is given special attention in this paper due to lack of sufficient research in this area and in spite of its important role in the fuel cell performance. To the authors’ knowledge, this is the first work focusing on developing control-oriented models which are capable of capturing important open-cathode fuel cell dynamics and the interactions between them. All of these models are validated experimentally on a small scale open-cathode fuel cell system. Finally, control challenges that can be formulated using the proposed models are discussed.

Keywords: Automotive, Power systems, Numerical algorithms
Abstract: This paper uses Fisher information to quantify the identifiability of internal resistance and charge capacity for a first-order nonlinear equivalent-circuit model of a lithium-ion battery undergoing periodic cycling. The paper derives analytic Cramer-Rao bounds on the variances with which a maximum likelihood estimator can determine these parameters in the presence of white and Gaussian voltage measurement noise. This mathematical analysis shows that the challenge of battery parameter identifiability, already recognized in the literature for higher-order electrochemical battery models, is fundamentally present even for much simpler equivalent circuit models. The analysis also quantifies the degree to which the sensitivity of battery open-circuit voltage with respect to state of charge affects parameter identifiability. The paper serves as a first cut analysis of the accuracy with which one can determine two battery state-of-health metrics – namely, power and capacity fade – from periodic cycling tests.

Keywords: Agents-based systems, Cooperative control
Abstract: A multi-agent system is considered that is tasked with the objective of approaching a predetermined target from a desired region to minimize detection, and then simultaneously converging at the target. Only a subset of agents is informed of the bearing to the target and the desired common arrival time; other agents only rely on local communication with neighboring agents for coordination. A balanced containment control is achieved that restricts the motion of the group within a desired region (i.e., a circular sector) while evenly spacing the agents' orientations. To enable simultaneous arrival, a consensus algorithm is designed for agents to coordinate their arrival time by achieving consensus with the informed agents. Only local feedback (i.e., orientation information and estimated arrival time) from neighboring agents is used to navigate the group, and no position information (either absolute or relative position between agents) is required, allowing the multi-agent system to operate in a GPS denied area. Simulation results demonstrate the performance of the developed approach.

Keywords: Agents-based systems, Cooperative control, Backstepping
Abstract: This paper introduces distance-based control laws for the multi-agent formation maneuvering and target interception problems using a double-integrator agent model and rigid graph theory. The proposed controls consist of a formation acquisition term, dependent on the graph rigidity matrix, and a formation maneuvering or target interception term. The control laws are only a function of the relative position/velocity of agents in an infinitesimally and minimally rigid graph, the agent's own velocity, and either the desired velocity of the formation or the target's relative position to the leader and velocity. The target interception control includes a variable structure-type term to compensate for the unknown target acceleration. A Lyapunov-based stability analysis is used to prove that the control objectives are met.

Keywords: Agents-based systems, Cooperative control, Indirect adaptive control
Abstract: We formulate a graph rigidity-based, adaptive formation control law for multiple robotic vehicles moving on the plane that explicitly accounts for the vehicle dynamics while allowing for parametric uncertainty. We consider a class of underactuated vehicles modeled by Euler-Lagrange-like equations of motion. The control is designed via backstepping while exploiting the structural properties of the system dynamics. A Lyapunov-like analysis shows that the desired formation is acquired asymptotically.

Keywords: Agents-based systems, Cooperative control, Autonomous systems
Abstract: In this paper, the flocking problem in a network of double-integrator agents with angularly limited fields of view (FOV) is investigated. The conic-shaped angular FOVs impose sensing limitations on every agent in the network. To increase the sensing capability of agents and preserve network connectivity, the FOV of every agent rotates with a sufficiently fast angular velocity. The problem is formulated in the framework of distributed switched nonlinear systems to address the switching topology of the network. Potential-based control inputs are subsequently designed as a combination of alignment and attractive/repulsive forces, such that the velocity vector of each agent exponentially reaches a certain neighborhood of a given desired velocity vector and the inter-agent collision is avoided. The efficacy of the proposed control strategy is confirmed by the simulation results.

Keywords: Agents-based systems, Autonomous systems, Cooperative control
Abstract: This paper proposes distance-based adaptive for- mation control laws for the leader-follower system. The de- veloped controller makes all the agents maintain the desired formation and move with a constant reference velocity in a plane. It is assumed that there are one leading and two following agents. The leading agent knows the reference velocity whereas the follower does not know the velocity of other agents. Thus, to move in a formation group, the controller for the follower estimates the reference velocity. An adaptive method is used in the estimation process. The stability and convergence of the formation are proved by using Lyapunov stability analysis and Barbalat’s lemma. Simulations results are included to illustrate the validity of the developed theories.

Keywords: Agents-based systems, Nonlinear systems
Abstract: The problem of steering a team of agents from their initial positions to a predefined end-configuration while avoiding collisions is formulated as a differential game. A method for approximating the solution of the differential game is then presented, providing epsilon-Nash strategies.It is shown that approximate solutions are sufficient to guarantee that the task of reaching the final configuration while avoiding collisions is achieved. The theory is illustrated by simulations.

Keywords: Mechatronics, Robust control, Control applications
Abstract: In this article, the Active Disturbance Rejection Control scheme, using a Generalized PI disturbance observer based control for the stabilization problem of the Furuta pendulum is proposed. The control scheme assumes a limited knowledge of the system, using a simplified model of the Furuta pendulum consisting in a disturbed chain of two integrators.

The control scheme is experimentally tested, showing good results in the stabilization error, which is restricted to a small region of the origin. A comparison study with respect to a linear control scheme is also carried out.

Keywords: Mechatronics, Mechanical systems/robotics, Control applications
Abstract: A trapezoidal velocity profile is widely used in industrial machines all over the world in particular for point-to-point motion. This paper deals with residual vibration suppression and energy saving for industrial machines operated by such a simple trapezoidal velocity profile. Because many industrial machine controllers currently in use allow to implement only such a simple motion trajectory, the proposed method has significant advantage on the implementation to these machines all over the world, and can contribute to recent environmental and energy-shortage problems. This paper first derives necessary and sufficient conditions for suppressing residual vibration by using a trapezoidal velocity profile for industrial machines that have typical second-order dynamics. Next, an equation for calculating energy consumed in point-to-point motion based on a trapezoidal velocity profile is presented. Simulation and experiment demonstrate the effectiveness of the proposed analytical results.

Keywords: Mechatronics, Nonlinear systems
Abstract: In this work we study the problem of rope sway dynamics control for elevator systems. We formulate this problem as a bilinear control problem and propose nonlinear controllers based on Lyapunov theory, to stabilize the rope sway dynamics. We study the stability of the proposed controllers, and test their performances on a numerical example.

Keywords: Emerging control theory, Nonlinear systems, Output feedback
Abstract: This paper presents an output feedback shared-control algorithm for fully-actuated, linear, mechanical systems. The feasible configurations of the system are described by a group of linear inequalities which characterize a convex admissible set. The properties of the shared-control algorithm are established with a Lyapunov-like analysis. Simple numerical examples demonstrate the effectiveness of the strategy.

Keywords: Mechatronics, Switched systems, LMIs
Abstract: In this paper, we introduce the split-path nonlinear integrator (SPANI) as a novel nonlinear filter designed to improve the transient performance of linear systems in terms of overshoot. In particular, this nonlinear controller targets the well-known trade-off induced by integral action, which removes steady-state errors due to constant external disturbances, but deteriorates transient performance in terms of increased overshoot. The rationale behind the proposed SPANI filter is to ensure that the integral action has, at all times, the same sign as the closed-loop error signal, which, as we will show, enables a reduction in overshoot (i.e., improves transient performance). The resulting closed-loop dynamics can be described by a continuous-time switched dynamical system, for which we will provide sufficient conditions for stability. Furthermore, we illustrate the effectiveness, the design and the tuning of the proposed controller in simulation examples.

Keywords: Feedback linearization, Nonlinear systems, Mechatronics
Abstract: Hybrid stepper motors are often used as actuators in automation and handling. To overcome their main drawbacks, step-wise motion and poor robustness against load disturbances, closed loop control can be applied. The concept of differential flatness offers powerful tools for a wide range of linear and nonlinear systems. The differential flatness property of the nonlinear MIMO model of a hybrid stepper motor is proven and a flat output is determined constructively as solution to a set of partial differential equations. By use of the flat output, an asymptotic tracking control is designed and evaluated in simulation and experiments.

Keywords: Delay systems, Biomedical
Abstract: In earlier work, an LQR tracking control of a delay differential equation model was used in the analysis of nanoparticle dosing strategies for cancer therapy. In this work, comparisons are made between the LQR-controlled system, but with modified dose, and two other controlled systems. One system investigated uses Dirac delta functions to achieve a discrete dosing strategy. The other system under consideration uses a PID tracking control because it is a common controller employed in the engineering, industrial, and lab settings. These different methods of tracking to a specific therapeutic target, though all effective, produce different results with different control efforts required. In this work, all of these methods with their respective results are analyzed and discussed.

Keywords: Delay systems, Distributed parameter systems, LMIs
Abstract: In this paper, we present an algorithmic approach to the construction of Lyapunov functions for infinite-dimensional systems. This paper unifies and significantly extends many previous results which have appeared in conference and journal format. The unifying principle is that any linear parametrization of operators in Hilbert space can be used to construct an LMI parametrization of positive operators via squared representations. For linear systems, we get positive linear operators and hence quadratic Lyapunov functions. For nonlinear systems, we get nonlinear operators and hence non-quadratic Lyapunov functions. Special cases of these results include positive operators defined by multipliers and kernels which are: polynomial; piecewise-polynomial; or semi-separable and apply to systems with delay; multiple spatial domains; or mixed boundary conditions. We also introduce a set of efficient software tools for creating these functionals. Finally, we illustrate the approach with numerical examples.

Keywords: Distributed parameter systems, Estimation, Computational methods
Abstract: In this paper, we investigate the properties of a complex nonmagnetic material through the reflectance, where the permittivity is described by a mechanism model in which an unknown probability measure is placed on the model parameters. Specifically, we consider whether or not this unknown probability measure can be determined from the reflectance or the derivatives of the reflectance, and we also investigate the effect of measurement noise on the estimation. The numerical results demonstrate that if only the reflectance can be observed, then the distribution form cannot be recovered even in the case where the measurement noise level is small. However, if both the reflectance and the derivative of the reflectance can be observed, then the estimated distribution is reasonably close to the true one even in the case where the measurement noise level is relatively high.

Keywords: Distributed parameter systems, Flexible structures, Optimization
Abstract: This work considers a class of second order infinite dimensional parameter systems that often model flexible structures and it is assumed that a number of identical such systems are desired to be synchronized. Using output feedback controllers, the resulting coupled systems are brought in an aggregate form that is conducive to optimization of the synchronization gains. Using the “closed-loop energy” as a suitable optimization index, the choice of the synchronization reduces to the minimization of the optimization index, which eventually is described by the trace of the solution to a parameterized Lyapunov operator equation. Extensive numerical studies on a network of four one-dimensional cantilevered beams provides further insight on the synchronization control.

Keywords: Distributed parameter systems
Abstract: In this work a PDE backstepping-based control law for one-dimensional unstable heat equation with time-varying spatial domain is developed. The underlying parabolic partial differential equation (PDE) with time-varying domain is the model emerging from process control applications such as crystal growth. In backstepping control law synthesis, a characteristic feature is that the PDE describing the transformation kernel of the associated Volterra integral is time-dependent. In this work, the kernel PDE is solved numerically and the state-feedback controller is simulated for the application of temperature regulation in the Czochralski crystal growth process.

Keywords: Indirect adaptive control, Distributed parameter systems, Process control
Abstract: We develop an adaptive output-feedback controller for a wave PDE in one dimension with actuation on one boundary and with an unknown anti-damping dynamics on the opposite boundary. This model is representative of drill string torsional instabilities arising in deep oil drilling, for which the model of bottom interaction with the rock is poorly known. The key feature of the proposed controller is that it requires only the measurements of boundary values and not of the entire distributed state of the system. Our approach is based on employing Riemann variables to convert the wave PDE into a cascade of two delay elements, with the first of the two delay elements being fed by control and the same element in turn feeding into a scalar ODE. This enables us to employ a prediction-based design for systems with input delays, suitably converted to the adaptive output-feedback setting. The result’s relevance and ability to suppress undesirable torsional vibrations of the drill string in oil well drilling systems is illustrated with simulation example.

Keywords: Building and facility automation, Decentralized control, Predictive control for linear systems
Abstract: Multi-evaporator vapor compression systems (ME-VCSs) are becoming widely used to meet the cooling needs for multiple thermal loads via a single system. As the number of evaporators increases, the size of these systems can make a centralized control approach impractical and computationally expensive; thus, motivating a decentralized control design. Linear gray-box modeling techniques show that ME-VCSs have a distinct underlying structure between the actuators and dynamic states known as a block arrow structure (BAS). This structure captures the high degree of coupling found in ME-VCSs, which can lead to poor decentralized control performance. This paper presents a partially decentralized model predictive control strategy which directly considers the coupling in the system when making control decisions by exploiting the BAS. The gray-box modeling approach and the decentralized nature of this BAS control strategy prove scalable to n-evaporator systems. Through simulated case studies, it is shown that this BAS control strategy can approximate the performance of a centralized control approach for ME-VCSs while significantly reducing computational costs.

Keywords: Building and facility automation, Modeling and simulation, Predictive control for linear systems
Abstract: This paper investigates the potential of coordinated air conditioning control for a simulated community of 900 homes with a high penetration of rooftop solar photovoltaic (PV) panels. The simulated community of homes is created from an extensive data set including home energy audits, homeowner surveys, and electricity meter measurements from actual homes in Austin, Texas, USA. Coordinated air conditioning control in the homes is simulated using a rolling horizon model predictive controller to minimize the peak demand of the community using both centralized and decentralized control methods. By manipulating thermostat set points, the controller takes advantage of the thermal mass of the buildings to store thermal energy. In all cases considered, the centralized controller outperforms the decentralized controller, but both lead to significant reductions in peak electricity demand. We find that decentralized control achieves nearly the same peak reduction as the centralized control method when all homes have rooftop PV. We also find that coordinated air conditioning control achieves a marginally smaller benefit as the penetration of rooftop PV increases.

Keywords: Building and facility automation, Predictive control for linear systems, Robust control
Abstract: Model uncertainty limits the utilization of Model Predictive Controllers (MPC) to minimize building energy consumption. We propose a new Robust Model Predictive Control (RMPC) structure to make a building controller robust to model uncertainty. The results from RMPC are compared with those from a nominal MPC and a common building Rule Based Control (RBC). The results are then used to develop a methodology for selecting a controller type (i.e. RMPC, MPC, and RBC) as a function of building model uncertainty. RMPC is found to be the desirable controller for the cases with an intermediate level (30%-67%) of model uncertainty, while MPC is preferred for the cases with a low level (0-30%) of model uncertainty. A common RBC is found to outperform MPC or RMPC if the model uncertainty goes beyond a certain threshold (e.g. 67%).

Keywords: Predictive control for linear systems, Process control, Control applications
Abstract: A model predictive control (MPC) scheme is introduced to directly control the electrical power consumption of large-scale refrigeration systems. Deviation from the baseline of the consumption is corresponded to the storing and delivering of thermal energy. By virtue of such correspondence, the control method can be employed for regulating power services in the smart grid. The proposed scheme contains the control of cooling capacity as well as optimizing the efficiency factor of the system, which is in general a nonconvex optimization problem. By introducing a fictitious manipulated variable, and novel incorporation of the evaporation temperature set-point into optimization problem, the convex optimization problem is formulated within the MPC scheme. The method is applied to a simulation benchmark of large-scale refrigeration systems including several medium and low temperature cold reservoirs.

Keywords: Predictive control for nonlinear systems, Optimization, Building and facility automation
Abstract: In this paper, two alternatives approaches to model predictive control (MPC) are compared and contrasted for the role of zone temperature controller - the commonly used linear formulation (LMPC) and rather unconventional nonlinear formulation (NMPC). The economical focus is reflected by the performance criterion being a combination of the comfort requirements and the monetary cost penalties (price of the consumed hot water and the electricity needed to deliver the water to the building) of the controlled inputs. With this formulation, the optimal controller drives toward minimization of the real price rather than minimization of abstract quantities. It turns out that the NMPC is able to attack the cost minimization directly while retaining a compact optimization formulation as opposed to the suboptimal linear alternative. A considerable part of the superiority of the NMPC can be owed also to the use of a nonlinear model that captures the nonlinear building dynamics much more accurately than the linear models. Thanks to an enhanced search step choice introduced in this paper, the NMPC outperforms both the LMPC and the conventional controller significantly even under severe computational restrictions which demonstrates its strong practical applicability.

Keywords: Building and facility automation, Control applications, Emerging control applications
Abstract: We study control-oriented methods of improving energy efficiency of ``under-actuated'' heating, ventilation, and air-conditioning) systems in commercial buildings where multiple rooms are served by a single variable air volume box. Two novel control algorithms are studied: one is a rule-based feedback controller that uses real-time occupancy measurement, and the other is based on model predictive control (MPC) that uses predictions of occupancy and other exogenous inputs. We focus our attention on ``under-actuated'' zones in which flow and temperature of ventilation air to individual rooms in a zone cannot be independently controlled. These have not been studied in the literature but are quite common in commercial buildings. Our study finds that under-actuated zones can also offer significant energy savings---even when occupancy in the rooms is vastly different---when independent heating is available for each room. Energy efficiency of simple, rule-based controllers that use occupancy measurements was found to be highly dependent on choice of temperature constraints during unoccupied times. Depending on this choice, MPC may or may not perform better than rule-based control.

Keywords: Estimation, Modeling and simulation, Embedded systems
Abstract: Estimation and filtering form an important component of most modern control systems. Techniques such as extended Kalman filters and particle filters have been successfully utilized for estimation in many different applications. Integrators derived from discrete mechanics possess desirable numerical properties such as stable long-time energy behavior, exact constraint satisfaction, and accurate statistical calculations. In the present work, we leverage these features by utilizing a variational integrator derived from discrete mechanics within extended Kalman filters and particle filters. By filtering real experimental data from the nonlinear, underactuated planar crane problem we demonstrate that the linearizations available through the discrete mechanics framework increase the accuracy of uncertainty estimates provided by an extended Kalman filter, especially when operating at low frequencies. Additionally, we illustrate situations where particle filter performance is increased through the statistics-preserving properties provided by the variational integrator.

Keywords: Kalman filtering, Sensor fusion, Optimization
Abstract: In this paper we study the problem of scheduling sensors to estimate the state of a linear dynamical system. The estimator is a Kalman filter and we seek to optimize the a posteriori error covariance over an infinite time horizon. We characterize the exact conditions for the existence of a schedule with uniformly bounded estimation error covariance. Using this result, we construct a scheduling algorithm that guarantees that the error covariance will be bounded if the existence conditions are satisfied. We call such an algorithm complete. We also show that the error will die out exponentially for any detectable LTI system. Finally, we provide simulations to compare the performance of the algorithm against other known techniques.

Keywords: Estimation, Optimal control, Nonlinear systems
Abstract: This paper develops an optimization method to synthesize trajectories for use in the identification of system parameters. Using widely studied techniques to compute Fisher information based on observations of nonlinear dynamical systems, an infinite-dimensional, projection-based optimization algorithm is formulated to optimize the system trajectory using eigenvalues of the Fisher information matrix as the cost metric. An example of a cart-pendulum simulation demonstrates a significant increase in the Fisher information using the optimized trajectory with decreased parameter variances shown through Monte-Carlo tests and computation of the Cramer-Rao lower bound.

Keywords: Estimation, Uncertain systems, Observers for linear systems
Abstract: This paper proposes a new method to design a scalar dynamical functional observer based feedback control, for a class of linear sampled-data systems with additive disturbances. The control action consists of the function of system state and disturbance components. The design of the function of state is based on the pole-placement technique and the function of disturbance is for the complete disturbance-counteraction on system performance. This work is based on the combination of functional observer design and multirate output sampling technique. Necessary and sufficient conditions for existence of the proposed controller are derived and the design procedure is given. It is shown that the proposed method relaxes some of the conditions of disturbance decoupled functional observer design, provided that the variation of disturbance in the two consecutive sampling instances is not significant. Numerical example is considered to illustrate the design procedure and its simplicity.

Keywords: Estimation, Kalman filtering, Adaptive systems
Abstract: Discontinuous systems have been increasingly paid attention since it can be found in various physical and biological systems. In this paper we consider an effective estimation algorithm for system with multiple discontinuities. We propose that a discontinuous-right-hand-side of state equation is approximated as multiplication of known discontinuous basis function and parameter that evolves in time. The basis function is selected in such a way that stability of estimation is guaranteed. Computational behavior (chattering) on sliding surface is investigated. We use continuous-discrete unscented Kalman filter to estimate the proposed model. A particular feature is included to the algorithm i. e. when system state orbits along sliding surface, estimation is switched to computation of deterministic model as it is shown unobservable by using Filippov's convex method. In order to obtain accurate computation (avoiding chattering) along sliding surface, we use the extension of the method. Simulation results of two application examples are provided to show the robustness of the algorithm.

Keywords: Control of communication networks, Networked control systems, Emerging control applications
Abstract: We study distributed estimation and tracking for radio environment mapping (REM). Comparing to existing REM using centralized methods, we provide a distributed solution eliminating the central station for map construction. Based on the random field model of the REM with shadow fading effects, we adopt consensus-based Kalman filter to estimate and track the temporal dynamic REM variation. The unknown parameters of REM temporal dynamics are estimated by a distributed Expectation Maximization algorithm that is incorporated with Kalman filtering. Our approach features distributed Kalman filtering with unknown system dynamics, and achieves dynamic REM recovery without localizing the transmitter. Simulation results show satisfactory performances of the proposed method where spatial correlated shadowing effects are successfully recovered.

Keywords: Markov processes, Machine learning
Abstract: The standard Markov Decision Process (MDP) framework assumes a stationary (or at least predictable) environment. Online learning algorithms can deal with nonstationary or unpredictable environments, but there is no notion of a state that might be changing throughout the learning process as a function of past actions. In recent years, there has been a growing interest in combining the above two frameworks and considering an MDP setting, where the cost function is allowed to change arbitrarily after each time step. However, most of the work in this area has been algorithmic: given a problem, one would design an algorithm from scratch and analyze its performance on a case-by-case basis. Moreover, the presence of the state and the assumption of an arbitrarily varying environment complicate both the theoretical analysis and the development of computationally efficient methods. This paper builds on recent results of Rakhlin et al. to give a general framework for deriving algorithms in an MDP setting with arbitrarily changing costs. This framework leads to a unifying view of existing methods and provides a general procedure for constructing new ones.

Keywords: Markov processes, Decentralized control, Cooperative control
Abstract: This paper introduces a probabilistic guidance approach for the coordination of swarms of autonomous agents. The main idea is to drive the swarm to a prescribed density distribution in the configuration space. Both non-collaborative and collaborative swarm guidance methods are considered. In the non-collaborative case, the probabilistic approach is decentralized and does not require communication between agents. Agents make statistically independent probabilistic decisions based solely on their own state, which ultimately guides the swarm to the desired density distribution. The probabilistic guidance idea is then extended to the collaborative case where there are sub-swarms of agents with different desired distributions. In this case, agents collaborate to decide on a common objective. This introduces collaboration at a higher level of decision making, and makes useful connections with consensus theory for networked agents.

Keywords: Markov processes, LMIs, Optimization
Abstract: This paper introduces a new approach for the synthesis of Markov chains with density upper bound constraints. The proposed approach is based on a new mathematical result that formulates the density upper bound constraints, known also as safety constraints, as linear, and hence convex, inequality constraints. It is proved that the new convex constraints are equivalent, necessary and sufficient, to the density upper bound constraints, which is the main contribution. Next, this result enabled the formulation of the Markov chain synthesis problem as an Linear Matrix Inequality (LMI) optimization problem with additional constraints on the steady state probability distribution, ergodicity, and state transitions. The LMI formulation presents an equivalent design formulation in the case of reversible Markov chains, that is, it is not conservative. When reversibility assumption is relaxed, the LMI condition is only sufficient due to the ergodicity constraint, i.e., it is conservative. Since LMI problems can be solved to global optimality in polynomial time by using interior point method (IPM) algorithms of convex optimization, the proposed LMI-based design approach is numerically tractable.

Keywords: Markov processes, LMIs, Robust control
Abstract: This paper investigates the problem of H-infinity static output feedback control design for discrete-time Markov jump linear systems (MJLS), assuming that the transition probability matrix is not precisely known, but affected by different classes of uncertainties: polytopic, bounded or completely unknown elements. All types of uncertainties are modeled through one single representation, expressed in terms of the Cartesian product of simplexes, called multi-simplex. The main novelty of the proposed design procedure is that, differently from previous approaches in the literature, parameter-dependent Lyapunov matrices are used to certify the closed loop stability with an H-infinity bound for the discrete-time MJLS. The proposed conditions are based on linear matrix inequality relaxations performed in two steps: the first step generates a parameter-dependent state feedback controller that is employed as an input for the second stage, which synthesizes a robust static output feedback gain assuring an H-infinity guaranteed cost. The proposed strategy can also cope with H-infinity state feedback control for discrete-time MJLS. Numerical examples illustrate the advantages of the proposed methodology when compared to other methods from the literature.

Keywords: Markov processes, Learning, Optimization algorithms
Abstract: We propose a simulation based algorithm, Empirical Value Iteration (EVI) algorithm, for finding the optimal value function of an MDP with infinite horizon discounted cost criteria when the transition probability kernels are unknown. Unlike simulation based algorithms using stochastic approximation techniques which give only asymptotic convergence results, we give provable and non-asymptotic performance guarantees in terms of sample complexity results: given epsilon > 0 and delta > 0, we specify the minimum number of simulation samples n(epsilon, delta) needed in each iteration and the minimum number of iterations t(epsilon, delta) that are sufficient for the EVI to yield, with a probability at least 1 - delta, an approximate value function that is at least epsilon close to the optimal value function.

Keywords: Markov processes, Automata
Abstract: We consider the synthesis of control policies over partially observable Markov decision processes with linear temporal logic specifications. We limit the search of policies over finite state controllers of a fixed size which leads to a Markov chain with free parameters, over which the probability of satisfaction of the specification can be maximized.

Keywords: LMIs, Cooperative control, Fault detection/accomodation
Abstract: In this paper, a reconfigurable control protocol for a linear multi-agent system seeking consensus in presence of actuator faults and saturations is investigated. The control protocol consists of two parts: healthy system controller and faulty system controller. The main objective of the healthy controller that is designed off-line is to determine the protocol which guarantees team consensus and minimizes the team performance index. On the other hand, the faulty system controller is designed on-line and aims at stabilizing the consensus error signals and optimizing the faulty team performance index subject to constrained input signals. Both centralized and semi-distributed architectures for control reconfiguration are developed. In the centralized scheme, the team is considered as a single unit and the central module reconfigures the entire system controllers, while in the semi-distributed approach faulty agent uses its immediate neighbours information (i.e. measurements and control gains) to reconfigure its controller.

Keywords: Mechanical systems/robotics, Autonomous systems
Abstract: We consider time-optimal path tracking for the class of pseudo-omnidirectional mobile robots. An Euler-Lagrange model of the robot dynamics is derived, and by writing it on special form a convex reformulation of the path-tracking problem can be utilized. This enables the use and regeneration of time-optimal trajectories during runtime. The proposed approach also incorporates avoidance of moving obstacles, which are unknown a priori. Using sensor data, objects along the desired path are detected. Subsequently, a new path is planned and the corresponding time-optimal trajectory for tracking of the generated path is found. The robustness of the method and its sensitivity to model errors are analyzed and discussed with extensive simulation results. Moreover, we verify the approach by successful execution on a physical setup.

Keywords: Uncertain systems, Robust control, Linear systems
Abstract: This paper addresses the problem of stabilizing discrete-time LTI systems with polytopic uncertainties via a static output feedback controller. We approach this problem using the recently developed periodically time-varying memory state-feedback controller design scheme, and a sufficient linear matrix inequality condition for the static output control design is proposed. Finally, numerical examples are given to demonstrate the usefulness of the proposed method.

Keywords: LMIs, Large scale systems, Computational methods
Abstract: Analysis questions in control theory are often formulated as Linear Matrix Inequalities and solved using convex optimisation algorithms. For large LMIs it is important to exploit structure and sparsity within the problem in order to solve the associated Semidefinite Programs efficiently. In this paper we decompose SDPs by taking advantage of chordal sparsity, and apply our method to the problem of constructing Lyapunov functions for linear systems. By choosing Lyapunov functions with a chordal graphical structure we convert the semidefinite constraint in the problem into an equivalent set of smaller semidefinite constraints, thereby facilitating the solution of the problem. The approach has the potential to be applied to other problems such as stabilising controller synthesis, model reduction and the KYP lemma.

Keywords: LMIs, Robust control, Nonlinear systems
Abstract: The question of how to establish closed-loop stability for systems involving unstable plants remains a difficult problem in control engineering. Although the notion of minimum gain and the corresponding Large Gain Theorem are designed specifically to tackle this issue, they are not yet widely used. This paper aims to facilitate the practical application of minimum gain and the Large Gain Theorem. An altered definition of minimum gain broadens the applicability of the Large Gain Theorem and the novel Minimum Gain Lemma provides linear matrix inequality conditions that imply and are often equivalent to a minimum gain for unstable linear time invariant systems. Both results play crucial roles in establishing a stability result that is robust with respect to reasonable changes in parameters for a numerical example involving an unstable plant.

Keywords: Fuzzy systems, Stochastic systems, LMIs
Abstract: The problem of Hankel-norm output feedback control is solved for a class of T-S fuzzy stochastic systems. The dynamic output feedback controller design technique is proposed by employing fuzzy-basis-dependent Lyapunov function approach and the conversion on the Hankel-norm controller parameters. Sufficient conditions are established to design the controllers such that the resulting closed-loop system is stochastically stable and satisfies a prescribed performance. The desired output feedback controller can be obtained by solving a convex optimization problem, which can be efficiently solved by standard numerical algorithms.

Keywords: Nonlinear systems, Computational methods
Abstract: We present a novel numerical technique for the computation of a Lyapunov function for nonlinear systems with an asymptotically stable equilibrium point. Our proposed approach constructs a continuous piecewise affine (CPA) function given a suitable partition of the state space, called a triangulation, and values at the vertices of the triangulation. The vertex values are obtained from a Lyapunov function in a classical converse Lyapunov theorem and verification that the obtained CPA function is a Lyapunov function is shown to be equivalent to verification of several simple inequalities. Furthermore, by refining the triangulation, we show that it is always possible to construct a CPA Lyapunov function. Numerical examples are presented demonstrating the effectiveness of the proposed method.

Keywords: Nonlinear systems, Adaptive systems, Power systems
Abstract: This paper tackles the problem of power regulation for wind turbines operating in the top region by an adaptive passivity based individual pitch control strategy. An adaptive nonlinear controller that ensures passivity of the mapping aerodynamic torque-regulation error is proposed, where the inclusion of gradient based adaptation laws allows for the on-line compensation of variations in the aerodynamic torque. The closed-loop equilibrium point of the regulation error dynamics is shown to be UGAS (uniformly globally asymptotically stable). Numerical simulations show that the proposed control strategy succeeds in regulating the power output of the wind turbine despite fluctuations of the wind field due to wake and turbulence, without overloading the pitch actuators.

Keywords: Nonlinear systems, Backstepping, Constrained control
Abstract: This paper addresses the problem of designing and experimentally validating a controller for steering an autonomous quadrotor vehicle along a time-dependent trajectory, while rejecting constant force disturbances. The proposed solution consists of a nonlinear adaptive state feedback controller that asymptotically stabilizes the closed-loop system and ensures perfect tracking even in the presence of force disturbances. We consider quadrotors vehicles actuated in thrust and angular velocity, and ensure that the actuation does not grow unbounded as a function of the position error. The constant force disturbance is estimated through the use of a smooth projector operator. We build a rapid prototyping and testing environment to streamline the implementation and the tuning of the control laws and present experimental results to demonstrate the performance and robustness of the proposed controller.

Keywords: Nonlinear systems, Biomedical, Modeling and simulation
Abstract: In this paper, we develop a nonlinear multicompartment lung mechanics model that accounts for nonlinearities in both the airway resistances and the lung compliances. Many models assume that the airway resistances for a lung mechanics system are constant over the entire range of air flows, and hence, pressure losses due to the airway resistances are assumed to be linear functions of the air flows. In the development of our nonlinear multicompartment lung model, we assume that the resistive losses are characterized by a Rohrer-type model, which can more accurately capture resistive losses as a function of the flows. Several illustrative numerical examples for a two-compartment lung model are presented and the response of the multicompartment lung model with nonlinear resistances and nonlinear compliances is compared to that of a multicompartment lung model with linear resistances and nonlinear compliances.

Keywords: Nonlinear systems, Automotive
Abstract: Control of BBW actuators is a challenging problem for many reasons, among which the most critical are the dead-zone due to the fluid reservoir and the limits of the electric motor that moves the pump. In this paper, a complete control architecture accounting for this nonlinear behavior is presented, where the main components are a linear controller, a dead-zone compensator and an anti-windup block, designed in a cascade fashion. With such a configuration, the achieved equilibrium point is guaranteed to be globally asymptotically stable and the overall system shows to be robust with respect to variations of the pressure-position curve. Some simulation examples are proposed to show the effectiveness of the proposed strategy.

Keywords: Nonlinear systems, Adaptive systems, Aerospace
Abstract: A new adaptive guidance law accounting for the autopilot lag as a second-order dynamics is proposed utilizing dynamic surface control. The target acceleration is considered as a bounded uncertainty, but its upper bound cannot be estimated as a priori. Considering the unknown parameter uncertainty, an update law is designed via Lyapunov approach. The missile acceleration command input is determined by a dynamic surface control law with a parameter update law. Some first-order low-pass filters are introduced into the designing process to avoid the occurrence of high-order derivatives of the tangential relative velocities in the expression of the guidance law such that the guidance law can be implemented in practical applications. Simulation results show that the new guidance law is highly effective.

Keywords: Biological systems, Stochastic systems, Filtering
Abstract: This paper presents a bio-inspired central pattern generator (CPG) architecture for feedback control of rhythmic behavior. The CPG circuit is realized as a coupled oscillator feedback particle filter. The collective dynamics of the filter are used to approximate a posterior distribution that is used to construct the optimal control input. The architecture is illustrated with the aid of a model problem involving pumping up of a swing, modeled as a parametrically forced pendulum. For this problem, the coupled oscillator particle filter is designed and its control performance demonstrated in a simulation environment.

Keywords: Autonomous systems, Sensor fusion, Aerospace
Abstract: A path planning and control method based on adaptive potential functions is presented for a group of unmanned aerial vehicles (UAVs) equipped with onboard sensors, and deployed to search and classify multiple targets. The proposed method plans the motion of the UAVs to support a primary sensing objective that, in this case, is to maximize the classification performance of the sensor measurements gathered by the UAVs over time. An adaptive potential function approach originally developed for ground robots is modified and employed as a guidance law for a class of rotary-wing UAVs that must also avoid obstacles located in a three-dimensional workspace. The simulation results show that, by this approach, a single UAV is capable of visiting targets that offer the best tradeoff between distance and measurement information value. Furthermore, simulations involving multiple UAVs deployed to classify the same set of targets show that, by this approach, there emerge a cooperative behavior by which the UAVs can react, as a group, to the targets' classification uncertainties.

Keywords: Estimation, Optimal control, Filtering
Abstract: Data Assimilation is central to Dynamic Data Driven Applications (DDDAS). The limitations of current techniques in the presence of non-linearity and dimensionality can, in principle, be ameliorated by effective non-Gaussian high dimensional inference in many areas within DDDAS, but particularly environmental applications. This paper presents an inference algorithm based on maximization of a quadratic form of mutual information that provides an optimization approach to filtering non-Gaussian non-linear systems. In particular, this is accomplished by using Kapur’s mutual information between model predictions and measurements based on Renyi entropy, and using ensemble-based kernel representations of probability mass functions. The effectiveness of the algorithm is demonstrated using the Lorenz- 95 model where it is seen outperforming contemporary ensemble filtering.

Keywords: Estimation, Uncertain systems, Information theory and control
Abstract: In this research, the effect of dynamic data measurement on source parameters estimation is studied. The concept of mutual information is exploited to identify the optimal location for each sensor, while performing the dynamic data measurement to improve accuracy of estimation. For validation purposes, an advection - diffusion simulation code, SCIPUFF (Second-order Closure Integrated PUFF) is being used as a modeling testbed to study the effect of using dynamic data measurement. A Bayesian estimation framework is being used to characterize the source parameters, while data measurement is performed by mobile sensors, which are located based on the concept of maximizing the information content. As our numerical simulations show, using dynamic data measurement, based on maximum information collection, leads to textit{considerably} better estimates of source parameters.

Keywords: Information theory and control, Sensor fusion, Optimal control
Abstract: This paper describes the problem of multiple target tracking and localization using dynamic sensors such as Unmanned Ariel Vehicles. Often the location or path of the target is uncertain and has to be accounted along with UAV fuel costs while optimally planning the dynamic sensor trajectory. The coupled problem of optimal target state estimation and sensor path planning is computationally expensive and at times intractable. In this paper, an iterative sub-optimal control approach is proposed with the intent of a real-time application.

Keywords: Modeling and simulation, Identification, Reduced order modeling
Abstract: Many problems in dynamic data driven modeling deals with distributed rather than lumped observations. In this paper, we show that the Monge-Kantorovich optimal transport theory provides a unifying framework to tackle such problems in the systems-control parlance. Specifically, given distributional measurements at arbitrary instances of measurement availability, we show how to derive dynamical systems that interpolate the observed distributions along the geodesics. We demonstrate the framework in the context of three specific problems: (i) finding a feedback control to track observed ensembles over finite-horizon, (ii) finding a model whose prediction matches the observed distributional data, and (iii) refining a baseline model that results a distribution-level prediction-observation mismatch. We emphasize how the three problems can be posed as variants of the optimal transport problem, but lead to different types of numerical methods.

Keywords: Computational methods, Nonlinear systems, Filtering
Abstract: A method solving the recursive Bayesian estimation problem by means of orthogonal series representations of the involved probability density functions is proposed. The coefficients of the expansion for the posterior density are recursively propagated in time via prediction and update equations. The method has two main benefits: it provides high estimation accuracy at a relatively low computational cost and is highly amenable to parallel implementation. The parallelization properties of the method are analyzed and evaluated on a shared memory multicore processor. Up to 8 cores are employed in the numerical experiments and linear speedup is achieved. An application to a bearings-only tracking problem demonstrates the low computational cost of the method by providing the same accuracy as the particle filter but with significantly less computations.

Keywords: Estimation, Uncertain systems, Observers for linear systems
Abstract: The unknown inputs in a dynamical system may represent unknown external drivers, input uncertainty, state uncertainty, or instrument faults and thus unknown-input reconstruction has several wide-spread applications. In this paper we consider recursive reconstruction of unknown inputs with a delay for both square and non-square systems. That is, we develop filters that use current measurements to estimate past states and reconstruct past inputs. We further develop convergence results for these filters and show that the convergence of these filters are related to multivariable zeros of the systems. Finally we also show that existing unbiased minimum-variance filters are special cases of the proposed filters.

Keywords: Estimation, Networked control systems, Multivehicle systems
Abstract: We present the Bayesian consensus filter (BCF) for tracking a moving target using a networked group of sensing agents and achieving consensus on the best estimate of the probability distributions of the target’s states. Our BCF framework can incorporate nonlinear target dynamic models, heterogeneous nonlinear measurement models, non-Gaussian uncertainties, and higher-order moments of the locally estimated posterior probability distribution of the target’s states obtained using Bayesian filters. If the agents combine their estimated posterior probability distributions using a logarithmic opinion pool, then the sum of Kullback–Leibler divergences between the consensual probability distribution and the local posterior probability distributions is minimized. Rigorous stability and convergence results for the proposed BCF algorithm with single or multiple consensus loops are presented. Communication of probability distributions and computational methods for implementing the BCF algorithm are discussed along with a numerical example.

Keywords: Filtering, Estimation, Kalman filtering
Abstract: A novel approach to utilizing negative information to improve multiple hypothesis tracking (MHT) of extended objects is presented. Specifically, a missed detection of a tracked object is treated as an observation of object occlusion, the likelihood of which is described by an occlusion-based sensor detection model. This negative observation is used to update hypothesis weights in a method that is supplementary to any existing MHT framework, as it continues to inform the filter in the absence of traditional measurements. Experimental results are presented demonstrating that integrating this additional interpretation of the available information improves tracking performance.

Keywords: Filtering, Kalman filtering, Estimation
Abstract: In this paper, the feedback particle filter (FPF) algorithm is introduced for the continuous-discrete time nonlinear filtering problem. As with the continuous-time FPF, the continuous-discrete time algorithm i) admits an innovation error-based feedback control structure, and ii) requires a solution of an Euler-Lagrange boundary value problem (E-L BVP). These solutions are described in closed-form for the linear Gaussian filtering problem. For the general nonlinear non-Gaussian case, an algorithm is described to obtain an approximate solution of the E-L BVP. Comparisons are also made to the particle flow filter algorithm introduced by Daum and Huang.

Keywords: Fault detection/accomodation, Aerospace, Stochastic systems
Abstract: Model-based fault detection methods can be used to reduce the size, weight, and cost of safety-critical aerospace systems. However, the implementation of these methods is based on models. Therefore, disturbance and model uncertainty must be considered in order to certify the fault detection system. This paper considers the worst-case false alarm probability over a class of stochastic disturbances and model uncertainty. This is one analysis needed to assess the overall system reliability. The single step, worst-case false alarm probability is shown to be equivalent to a robust H2 analysis problem. Hence known results from the robust H2 literature can be used to upper bound this worst-case probability. Next, bounds are derived for the worst-case false alarm probability over multiple time steps. The multi-step analysis is important because reliability requirements for aerospace systems are typically specified over a time window, e.g. per hour. The bounds derived for the multi-step analysis account for the time correlations introduced by the system dynamics and fault detection filters. Finally, a numerical example is presented to demonstrate the proposed technique.

Keywords: Fault-tolerant systems, Aerospace, Variable-structure/sliding-mode control
Abstract: This paper presents the results from a novel FTC scheme designed for one of the RECONFIGURE benchmark problems. The scheme retro-fits an integral sliding mode control allocation scheme to the existing controller to achieve robustness and tolerance against faults/failures, in order to maintain nominal load factor tracking performance as well as the angle of attack protection. The scheme takes into consideration imperfections in the estimation of the actuator effectiveness level, which is used as part of the control allocation scheme. An adaptive element has also been used in order to give wide coverage of the flight envelope. Stability tests based on the Popov criterion are also presented which are less conservative than earlier work in this area. Preliminary simulation results using the RECONFIGURE benchmark model show the efficacy of the proposed scheme.

Keywords: Fault-tolerant systems, Flight control, Supervisory control
Abstract: A multi-level reconfiguration framework is proposed for fault tolerant control of overactuated aerial vehicles, where the levels indicate how much authority is given to the reconfiguration task. On the lowest, first level the fault is accommodated by modifying only the actuator/sensor configuration, so the fault remains hidden from the baseline controller. A dynamic reallocation scheme is applied on this level. The allocation mechanism exploits the actuator/sensor redundancy available on the aircraft. In case the fault cannot be managed at the actuator/sensor level the reconfiguration process has access the baseline controller. Based on the LPV control framework, this is done by introducing fault-specific scheduling parameters. The baseline controller is designed to provide acceptable performance level along all fault scenarios coded in these scheduling variables. The decision of which reconfiguration level has to be initiated in response to a fault is determined by a supervisor unit. The method is demonstrated on the full six degrees of freedom nonlinear simulation model of the GTM UAV.

Keywords: Fault detection/accomodation, Control software, Aerospace
Abstract: In this paper, we present a domain specific process to enable the verification of observer-based fault detection software. Observer-based fault detection systems, like control systems, yield invariant properties of quadratic types. These quadratic invariants express both safety properties of the software, such as the boundedness of the states, and correctness properties, such as the absence of false alarms from the fault detector. We seek to leverage these quadratic invariants, in an automated fashion, for the formal verification of the fault detection software. The approach, named credible autocoding framework, can be characterized as autocoding with proofs. The process starts with the fault detector model, along with its safety and correctness properties, all expressed formally in a synchronous modeling environment such as Simulink. The model is then transformed by a prototype credible autocoder into both code and analyzable annotations for the code. We demonstrate the credible autocoding process on a running example of an output observer fault detector for a 3 degree-of-freedom (3DOF) helicopter control system.

Keywords: Fault-tolerant systems, Sensor fusion, Kalman filtering
Abstract: The fault-tolerant integrated navigation is studied for the unmanned aerial vehicle (UAV) system. Considering different navigation accuracy and observability degree of each system state component, the vector distribution coefficients, which ensure the satisfaction of the law of conservation of information, are firstly developed for federated Kalman filter to get better navigation accuracy. Then, fault-tolerant navigation filters are proposed to detect and address the possible faults during the navigation period. Finally, aided by the relative bearing and speed information on the leader UAV, two navigation simulation examples are conducted to illustrate the effectiveness of the proposed method.

Keywords: Fault detection/accomodation, Identification, LMIs
Abstract: This paper addresses the design of input signals for the purpose of discriminating among a finite set of models dynamic systems within a given finite time interval. A motivating application is fault detection and isolation. We propose several specific optimization problems, with objectives or constraints based on signal power, signal amplitude, and probability of successful model discrimination. Since these optimization problems are nonconvex, we suggest a suboptimal solution via a random search algorithm guided by the semidefinite relaxation (SDR) and analyze the accuracy of the suboptimal solution. We conclude with a simple example taken from a benchmark problem on fault detection for wind turbines.

Keywords: Aerospace, Constrained control, Control applications
Abstract: This paper develops a new state feedback model reference adaptive control approach for uncertain systems with gain scheduled reference models in a multi-input multi-output (MIMO) setting with constrained control inputs. A single Lyapunov matrix is computed for multiple linearizations of the nonlinear closed-loop gain scheduled reference system, using convex optimization tools. This approach guarantees stability of the closed-loop gain scheduled reference model. Adaptive state feedback control architecture is then developed, and its stability is proven for the case with constrained control inputs. The resulting closed-loop system is shown to have bounded solutions with bounded tracking error, with the proposed stable gain scheduled reference model. Sufficient conditions for ultimate boundedness of the closed-loop system are derived. A semi-global stability result is proved with respect to the level of saturation for open-loop unstable plants while the stability result is shown to be global for open-loop stable plants. Simulation results show that the developed adaptive controller can be used effectively to control a degraded turboshaft engine for large thrust commands, with guaranteed stability and proper tracking performance.

Keywords: Adaptive systems, Linear systems, Uncertain systems
Abstract: This paper presents an L1 adaptive output feedback augmentation of a Model Reference Controller for a class of unknown single-input single-output, linear time-invariant systems. Similar to prior solutions in L1 adaptive control theory, the feedback structure consists of a predictor, adaptation laws and a low-pass filter. The closed-loop system achieves arbitrarily close tracking of the input and the output signals of the reference system. The proposed control strategy has been implemented and simulated on a longitudinal autopilot for a missile model. Simulation results show the performance of the adaptive control law in the presence of parameter uncertainties and input disturbance

Keywords: Adaptive systems, Direct adaptive control, Switched systems
Abstract: The adaptive state feedback control problem for multivariable piecewise linear systems with switched parameters is considered in this paper. A direct state feedback model reference adaptive control (MRAC) scheme is developed based on high frequency gain matrix decompositions. This scheme relaxes a key design condition, namely the high frequency gain matrices sharing a common bm{L} matrix in their LDS decompositions, required by an existing adaptive control scheme. Under the usual slow system parameter switching condition, closed-loop stability (signal boundedness) and small output tracking error in the mean square sense are achieved. The proposed scheme is simulated on linearized NASA GTM models to demonstrate its effectiveness in tracking performance improvement.

Keywords: Aerospace, Decentralized control, Control applications
Abstract: Control theoretic concepts for decentralized adaptive control of uncertain systems with gain scheduled reference model is developed. For each subsystem a single Lyapunov matrix is computed, using convex optimization tools, for multiple linearizations near equilibrium and non-equilibrium points of the nonlinear closed-loop gain scheduled reference subsystem. This approach guarantees stability of the closed-loop gain scheduled reference model. Then, decentralized adaptive state feedback control architecture is developed and its stability is proved. Specifically, the resulting closed-loop system is shown to have bounded solutions with bounded tracking error for all the subsystems. Simulation results for two different models of a turboshaft engine, including the nominal engine model and an engine model with a new core subsystem, illustrate the possibility of stable decentralized adaptive control of gas turbine engines with proper tracking performance.

Keywords: Autonomous systems, Direct adaptive control, Aerospace
Abstract: This paper presents an adaptive total energy control approach to regulate the airspeed and the flight path angle of a fixed wing aircraft. The control strategy results from a nonlinear description of the total energy control system approach, and only requires the knowledge of the structure of the aerodynamic drag coefficient as well as the sign of the aerodynamic control derivative related to the variation of the pitch moment due to the elevator input. The aerodynamic characteristics of the experimental aircraft were obtained from an open source computational fluid dynamics software. Experimental results are presented to verify the performance of the proposed controller.

Keywords: Adaptive systems, Indirect adaptive control, Aerospace
Abstract: Few methods have been proposed for designing adaptive controllers for underactuated systems with constant, unknown parameters. This is primarily because nonlinear underactuated systems are typically not feedback linearizable; most model-reference adaptive control approaches for nonlinear systems, especially those involving Lyapunov analysis, rely heavily on feedback linearization to guarantee stability. Self-tuning regulators do not have this limitation, but these more flexible methods are often computationally intractable and usually lack a proof of stability.

Here, we propose an alternative adaptive control design procedure which can handle underactuated systems of moderate dimension (< 12). By making use of recent advances in sums-of-squares optimization, we build upon the work done in Topcu et al. and Majumdar et al., to design adaptive controllers with verified robustness to parameter uncertainty, and time-varying adaptive controllers with guaranteed finite-time performance along system trajectories. We demonstrate our algorithm on three simulated underactuated systems - the Acrobot and cart-pole systems with unknown viscous friction terms and the perching glider with unknown aerodynamic coefficients.

Keywords: Power systems, Distributed parameter systems, Large scale systems
Abstract: In this paper, we present a set of distributed algorithms to estimate the oscillatory electro-mechanical eigenvalues of large power system networks using real-time measurements of phase angles and frequencies. We consider the underlying network to exhibit a strong coherency structure resulting in a two-time-scale behavior of the states. Our goal is to develop a distributed strategy by which only the slow eigenvalues, or the inter-area oscillation modes, can be estimated. Assuming a reliable bound for the slow frequencies, we first pass the actual measurements from the full-order network through a bandpass filter, and use the filter outputs as the effective inputs to our distributed estimation routine. We integrate the centralized Prony method for modal extraction with two commonly-used distributed optimization algorithms, namely, distributed subgradient method (DSM), and alternating direction method of multipliers (ADMM), and show how the filtered outputs can be used in each case to asymptotically estimate the slow eigenvalues of interest. We illustrate our results using a IEEE 39-bus power system, and show the robustness of our methods in face of communication failures.

Keywords: Power systems, Decentralized control, Optimization
Abstract: Automatic generation control (AGC) regulates mechanical power generation in response to load changes through local measurements. Its main objective is to maintain system frequency and keep energy balanced within each control area so as to maintain the scheduled net interchanges between control areas. The scheduled interchanges as well as some other factors of AGC are determined at a slower time scale by considering a centralized economic dispatch (ED) problem among different generators. However, how to make AGC more economically efficient is less studied. In this paper, we study the connections between AGC and ED by reverse engineering AGC from an optimization view, and then we propose a distributed approach to slightly modify the conventional AGC to improve its economic efficiency by incorporating ED into the AGC automatically and dynamically.

Keywords: Power systems, Decentralized control, Optimization
Abstract: The paper analyzes the optimal response of an individual consumer, with a deferrable demand for electricity, to exogenous stochastic prices. The main goal of the paper is (i) to introduce a model where many realistic features are taken into account (such as the presence of bounds on power consumption, the possibility of curtailment, and time correlation in the price process) (ii) determine an explicit and accurate mathematical description for the consumption policy of a rational consumer, and (iii) investigate the salient properties of these characterizations.

Keywords: Power systems, Direct adaptive control, Modeling and simulation
Abstract: In this paper a neural network-based approximate dynamic programming method, namely direct heuristic dynamic programming (direct HDP), is applied to power system stability control. Direct HDP is a learning and approximation based approach to address nonlinear system control under uncertainty. In the present paper, real-time system responses provided by wide area measurement system (WAMS) are used to construct such controllers which are uniquely tailored for the problems under consideration. In addition, the controller learning objective is formulated as a reward function that reflects global characteristics of the power system low frequency oscillation under the consideration of coupling effect among system components. The contribution of the paper includes a convergence proof of the direct HDP algorithm using an LQR framework, as well as case study to illustrate the proposed learning control algorithm. The case study aims at providing a new solution to a difficult large scale system coordination problem where the China Southern Power Grid is used for.

Keywords: Power systems, Distributed parameter systems, Reduced order modeling
Abstract: In this paper we consider continuum models of power system networks divided into clusters, and present a model reduction method to derive the analytical expressions for frequency waves propagating from one cluster to another depending on the network topology. Previous results have modeled such electromechanical waves mostly for two-area radial transfer paths, i.e., for two-node line graphs. Our results extend this approach to a network of areas connected by equivalent transfer paths by defining an equivalent Sturm-Liouville eigenvalue equation for the clustered network. We derive analytical expressions for the equivalent damping and power spectrum of this eigenvalue problem, as well for the spatio-temporal kinetic and potential energy functions that may used for transient stability assessment of the clustered network model following a disturbance.

Keywords: Power systems, Decentralized control, Control of networks
Abstract: This paper focuses on reactive power flow and voltage stability in electrical grids. We provide novel analytical understanding of the solutions to the classic nonlinear polynomial equations describing the decoupled reactive power flow. As of today, solutions to these equations can be found only via numerical methods. Yet an analytical understanding would be beneficial to the rigorous design of future electrical grids. This paper has two main contributions. First, for sufficiently high reference voltages, we guarantee the existence of a high-voltage solution for the reactive power flow equations and provide its approximate analytical expression. We bound the approximation error in terms of network topology and parameters. Second, we consider a recently proposed droop control strategy for voltage stabilization in a microgrid equipped with inverters. For sufficiently high reference voltages, we prove the existence and the exponential stability of a high-voltage fixed point of the closed-loop dynamics. We provide an approximate expression for this fixed point and bound the approximation error in terms of the network topology and parameters. Finally, we validate the accuracy of our approximations through numerical simulation of the IEEE 37 standard test case.

Keywords: Control applications, Nonlinear systems, Linear parameter-varying systems
Abstract: In this paper we propose a nonlinear H_2 control to improve transient response in the position control and to reduce energy consumption for the position tracking of Sawyer motors. The proposed method consists of a nonlinear torque modulation with a commutation scheme and gain scheduling LPV controller. The position control problem for the Sawyer motor is reformulated into the LPV system optimal problem. The nonlinear models of the Sawyer motors are analyzed as a LPV system. However, in LPV formulation, each vertex to guarantee the controllability should be chosen because of relationship between tracking motion and yaw motion. Hence, stabilizing the inner-loop system with state feedback controller is designed to solve above problem. The control gain scheduling is determined by using H_2 control based on linear matrix inequality (LMI) approach. Since the proposed method is designed based on optimal control with gain scheduling based on LPV synthesis, the proposed method obtains both improved transient response and reduced energy consumption in the position control.

Keywords: Randomized algorithms, Robust control, Predictive control for linear systems
Abstract: This paper presents a novel sampling-based planner which guarantees robustness for linear systems subject to bounded process noise, localization error, and/or uncertain environmental constraints. The proposed algorithm extends RRT*, efficiently generating and optimizing robust, dynamically feasible trajectories. During planning, state constraints are individually tightened for robustness against future uncertainty, while the input constraints can be tightened in order to apply feedback policies within planning for reduced conservatism. Simulation results demonstrate identification of smooth, guaranteed-safe trajectories in complex scenarios subject to both internal and external uncertainty, including cases where the uncertainty may be asymmetric and/or non-convex.

Keywords: Optimal control, Learning, Biomedical
Abstract: In this study, we employ optimal control and tactile feedback to teach subjects how to balance a simulated inverted pendulum. The output of a Linear Quadratic Regulator (LQR) was converted to a vibratory teacher-signal and was provided as additional somatosensory feedback to the subjects. The LQR approach is consistent with an energy-saving strategy commonly observed during human motor learning. Our rationale for using the inverted pendulum as a criterion task is that this balance system requires the brain to solve many of the same problems encountered in simple tasks of daily living like transporting a glass of water to the mouth. Experimental results indicate that subjects who trained with the teacher-signal, performed significantly better than subjects who trained only with visual feedback. This result is promising and can be applied, among other fields, in rehabilitation to compensate for lost or compromised proprioception, commonly observed in stroke survivors.

Keywords: Constrained control, Predictive control for linear systems, Control applications
Abstract: We propose a design for soft landing control based on control invariant sets and receding horizon control. Soft landing control, which is of interest in several applications in aerospace, transportation systems, and factory automation, aims at achieving precise positioning of a moving object to a target position, while ensuring that the maximum velocity decreases as the target is approached. The resulting soft contact avoids damages and wear. In this paper, we formulate appropriate constraints and recast soft landing control as the generation of an admissible trajectory of the constrained system. Then, we compute a control invariant set and design a receding horizon control law that forces the state to remain in such set. Thus, the trajectories generated by the controller achieve soft landing, regardless of the controller cost function and horizon, also when the dynamics are uncertain. We demonstrate our approach by a case study in transportation systems.

Keywords: Wireless, PID control, Observers for linear systems
Abstract: This paper presents a wireless PID controller, known as PIDPlus. The same quality control as wired PID can be provided by PIDPlus using a wireless measurement, despite slower wireless measurement update and communication interruptions. The PIDPlus novel algorithm is based on a modification of the PID reset and rate calculation to account for non-periodic measurement updates. The paper presents and evaluates an alternate approach of PID control accounting for non-periodic measurements with a Kalman filter observer that has been modified for use with a wireless measurement. A second alternative shows how the Smith Predictor may be modified to work with a wireless measurement. Test results are presented that compare the PIDPlus performance to that achieved using these alternate approaches for a variety of operating conditions that may be encountered when using wireless transmitters.

Keywords: Cooperative control, Learning, Nonlinear systems
Abstract: A data-driven concurrent learning-based control law is developed for the synchronization of a leader-follower network of agents with uncertain nonlinear dynamics wherein only a subset of the follower agents is connected to the leader. The development is facilitated by the use of online data-driven adaptive update policies to approximately learn a distributed control law which satisfies a given performance metric without the need for persistence of excitation (PE). A neighbor-decoupled control structure is introduced which provides greater flexibility in the consideration of individual neighbors during synchronization and makes the control of each agent a differential game.

Keywords: Cooperative control, Decentralized control, Autonomous systems
Abstract: In this paper, we study the consensus problem for second-order heterogeneous multi-agent systems under a directed graph. Unlike the existing consensus algorithms for second-order multi-agent systems in which all agents are assumed to have common unit inertias or share common control gains, we allow the inertias and the control gains to be heterogeneous for each agent. Fully distributed consensus algorithms are proposed when there exist, respectively, absolute velocity damping and relative velocity damping. Moreover, an adaptive σ-modification scheme for the gain adaption is proposed, which renders smaller control gains and thus requires smaller amplitude on the control input without sacrificing the consensus convergence.

Keywords: Cooperative control, Linear systems, Networked control systems
Abstract: Collective behaviors of multi-agent systems over signed graphs find applications in a variety of scenarios including social networks, predator-prey dynamics, which however have not been adequately addressed as their counterparts with nonnegative graphs. This paper studies bipartite consensus problem of general linear multi-agent systems over signed digraphs. First, we show that for general linear agents, bipartite consensus over signed graphs and ordinary consensus over nonnegative graphs are equivalent. This indicates that prevailing consensus controllers for nonnegative graphs can be adopted to solve bipartite consensus problems. Based on this observation, an existing Riccati equation based cooperative tracking controller is extended to solve the bipartite consensus problem for general linear systems.

Keywords: Cooperative control, Decentralized control, Agents-based systems
Abstract: In this work, we address the connectivity maintenance problem for a team of mobile robots which move according to a given collective control objective. In our framework, the interaction among the robots is limited by a given visibility radius both in terms of sensing and communication. For this scenario, we propose a bounded control law which can provably preserve the connectivity of the multi-robot system over time even in the presence of any desired bounded control objective. Furthermore, we characterize the effects of the connectivity control term on the collective control objective, in terms of robustness of the desired control objective to the disturbance of the connectivity, by resorting to the set Input- to-State Stability framework (set-ISS). For the validation of the proposed bounded connectivity control law we consider the encirclement problem as an example of collective control objective. Simulations are provided to corroborate the theoretical results.

Keywords: Cooperative control, Delay systems, Nonlinear systems
Abstract: This paper investigates the consensus control of a class of Lipchitz nonlinear multi-agent systems with communication time delay. Upon exploring certain features of Laplacian matrix, global consensus control conditions are identified using Lyapunov method in the time domain. The proposed control only uses relative state information of the system. Numerical simulations validate the effectiveness of the proposed controller design and show its performance with different nonlinearities and time delays.

Keywords: Cooperative control, Control of communication networks, Multivehicle systems
Abstract: In this paper we develop an approach for the control of evolution of a multi agent system (MAS) based on a new local inter-agent communication protocol. The agents of the MAS occupy a compact region, have n-D dynamics, and are guided by n+ 1 special leader agents that can move independently. The rest of the agents, called followers, have the ability to update their positions via local communication with some adjacent agents. The special communication protocol is brought about by certain distance ratios that must be satisfied by points of a set that transforms as a homogeneous map. It is assumed that the leaders can and will determine on-line, in real time the desired intermediate configurations of the MAS. It is further assumed that the leaders cannot directly communicate such information to the follower agents in the MAS. The followers are expected to acquire that information through communication with n+ 1 local agents. The followers by moving in such a way to preserve, at all times, certain pre-computed distance ratios corresponding to the initial configuration of the MAS will learn the desired homogeneous maps. The proposed technique allows the MAS to (i) deform like a flexible body and/or (ii) undergo rigid body translations by satisfying certain key properties of homogeneous maps guaranteed by the special local inter-agent communication protocol.

Keywords: Automotive, Modeling and simulation, Simulation
Abstract: Homogeneous Charge Compression Ignition (HCCI) engines have the advantage of low Nitrogen Oxides (NOx) and soot emissions. In HCCI engines, a lean premixed air-fuel mixture is compressed until the temperature is high enough for combustion to occur. HCCI engines have a limited operating range and are limited by knock at high loads and misfire at low loads. They are without a direct source to initiate ignition so HCCI requires combustion timing control. Some of the factors that affect HCCI combustion timing are mixture composition, pressure and temperature at the time of inlet valve closing. One effective way to control HCCI combustion timing is Variable Valve Timing (VVT). VVT changes the amount of trapped residual gas and the effective compression ratio. These factors have a strong effect on HCCI combustion timing. One main advantage of VVT is that it is fast enough to handle rapid transients. Based on a simplified control oriented model that models the effect of trapped residual gas on combustion timing, a Feedforward/Feedback controller is designed for HCCI combustion timing control . The controller requires feedforward information of the valve timing and feedback information of the combustion timing. This controller tracks the desired combustion timing trajectory both in simulation and experiment by modulating the trapped residual gas using VVT actuation.

Keywords: Automotive
Abstract: Real-time model based control of Homogeneous Charge Compression Ignition (HCCI) engines faces a critical challenge of maintaining a perfect balance between model accuracy and computational load. In particular, currently available HCCI emissions models in the literature are highly computationally expensive for control applications. This paper develops a computationally efficient grey-box HCCI engine model for predicting Total Hydrocarbon (THC), Carbon Monoxide (CO), and Nitrogen Oxides (NOx). The grey-box model consists of a feed forward Artificial Neural Networks (ANN) model in combination with physical models for estimating combustion phasing and Indicated Mean Effective Pressure (IMEP). The emission model is experimentally validated over a large range of HCCI engine operation including 208 steady state test conditions. The validation results show that the grey-box model is able to predict NOx, CO, and THC with average relative errors less than 10%. Using a Genetic Algorithm optimization method along with the developed emission grey-box model, an optimum CA50 trajectory is obtained for every given load trajectory in order to minimize THC and CO emissions. A model-based controller is designed and tested on the grey-box virtual engine model for tracking IMEP and the optimum CA50 trajectories, while indirectly minimizing the engine emissions. Control results show that the developed grey-box model is of utility for real time HCCI control applications.

Keywords: Modeling and simulation, Automotive, Simulation
Abstract: Combustion phasing and work output are the two critical indicators for achieving and maintaining homogeneous combustion for homogeneous charge compression ignition (HCCI) engines. However, the complicated thermodynamic process and too many factors’ contribution keep the combustion phasing model from explicit. In this paper, polynomial models for combustion phasing and work output are proposed. The intake conditions, including intake manifold temperature, pressure and burned gas fraction as well as the fuel injection are chose as the 4 variables by whom the combustion phasing and work output are determined. The impacts of various compression ratios on the two indicators (represented by CA50 and IMEP) were investigated and have been classified into two groups: vertical shift and curve deformation and both can be well modeled and, therefore compensated.. The models are validated through high-fidelity simulations.

Keywords: Modeling and simulation, Supervisory control, Automotive
Abstract: A vehicle and driver model from [1] is here integrated with a finite state model describing mode switches among spark-ignited (SI) combustion and the advanced combustion modes spark assisted compression ignition (SACI) and homogeneous charge compression ignition (HCCI). The model is used to quantify the influence of mode switch fuel penalties on drive cycle fuel economy considering the federal test procedure (FTP-75) and highway fuel economy test (HWFET). The simulation under the assumed fuel penalties and dynamics shows a very small fuel loss due to harmful switches. Harmful switches are mode switches that lead to short stays in the advanced combustion, where the penalty in switching is greater than the benefit achieved after the switch. The simulations also highlight the benefits of integrating the SACI mode. Apart from extending the fuel efficiency improvements beyond the HCCI range, it is postulated that SACI combustion is easier to reach (lower fuel penalty and faster response than the HCCI-SI switch) from both SI and HCCI, creating a bridge and an economic destination in the overall speed-load space. The combined operation of SACI and HCCI leads to substantially higher improvements, especially for the FTP-75 drive cycle, than using either mode individually.

Keywords: Automotive, Control applications, Identification
Abstract: Presented is a feed-forward controller that reduces air-fuel (A/F) ratio excursions during a canister purge event. The controller is linear parameter-varying (LPV) model and estimates the necessary changes in the fuel pulse-width (FPW) based on a hydrocarbon (HC) sensor located in the purge line. Coordination between the purge fuel vapor arrival to the intake manifold and the fueling command is realized using a transport delay model. The proposed controller is experimentally validated. A significant reduction in A/F excursions is achieved during the steady state engine operating conditions associated with highway conditions.

Keywords: Adaptive systems, Optimization, Mechanical systems/robotics
Abstract: For many control applications, identifying an optimal operating point by maximizing/minimizing a performance function is important. This paper applies the extremum-seeking method to nonaffine, nonlinear discrete-time systems stabilized by an optimal adaptive controller. First, a novel averaging method is used for the nonlinear discrete-time systems to show that their output unique extrema are stable equilibrium points. Then, a singular perturbation method in discrete time is employed to show that the overall closed loop system will dynamically converge to the extremum. The applicability of this scheme is numerically verified on a Homogeneous Charge Compression Ignition (HCCI) model validated experimentally and expressed as an uncertain nonaffine, nonlinear discrete-time dynamic system. This proposed method is able to find an operating point that not only maximizes the engine’s efficiency, but also maintains the pressure rise rate (PRR) within the engine’s operating limits.

Keywords: Supervisory control, Hybrid systems, Autonomous systems
Abstract: This paper describes the design of a supervisory controller (supervisor) that manages controlled vehicles to avoid intersection collisions in the presence of uncontrolled vehicles. Two main problems are addressed: verification of the safety of all vehicles at an intersection, and management of the inputs of controlled vehicles. For the verification problem, we employ an inserted idle-time scheduling approach, where the “inserted idle-time” is a time interval when the intersection is deliberately held idle for uncontrolled vehicles to safely cross the intersection. For the management problem, we design a supervisor that is least restrictive in the sense that it overrides controlled vehicles only when a safety violation becomes imminent. We analyze computational complexity and propose an efficient version of the supervisor with a quantified approximation bound.

Keywords: Fluid power control, Backstepping, Nonlinear systems
Abstract: A comparison of two decentralised backstepping control strategies - with and without an integral part - is presented in this paper for the tracking control of a hydrostatic drive train, which is commercially used in working machines. An unknown leakage volume flow and a resulting load torque are taken into account as lumped disturbances. These disturbances and two unmeasurable state variables - the normalised swashplate angle and the normalised bent axis angle - are estimated by a nonlinear observer. Thereby, a high tracking accuracy can be achieved for the normalised bent axis angle and the angular velocity of the motor as controlled variables. The efficiency of the proposed controllers is demonstrated by both simulations and experiments.

Keywords: Nonholonomic systems, Constrained control, Autonomous systems
Abstract: This paper studies the tracking control of nonholonomic mobile robots with bounded velocities and accelerations. Two first-order filters driven by the tracking errors are developed to produce bounded and uniformly continuous feedback signals for the tracking controller. The feedback signals and their time derivatives are directly constrained by two filter parameters. A set of four inequality conditions on the filter parameters and a feedback parameter is derived to guarantee the satisfaction of the velocity and acceleration constraints. The system stability is proved by the Lyapunov stability theory. Simulation results are provided to verify the effectiveness of our proposed tracking controller.

Keywords: Fault-tolerant systems, Hierarchical control, Automotive
Abstract: This paper focuses on the design of control strategy in four-wheel independent drive (4WID) electric vehicles when motor drive systems fail. We proposed a control strategy to maintain the performance and stability for the 4WID electric vehicles with motor drive systems failure. Firstly, failure modes of motor drive systems are classified and analyzed as Failure-Driving mode and Failure-Stopping mode. Secondly, a two-layer-control-model (upper layer and lower layer) is presented to maintain the performance and stability of the vehicle by controlling the normal motor drive systems for Failure-Driving mode; In the upper layer, fuzzy logic direct yaw-moment control (DYC) is proposed .In the lower layer, a control algorithm is designed to distribute torque to motor drive systems without failure. The effectiveness of the control strategies have been examined using a driving simulator system.

Keywords: Nonholonomic systems, Autonomous systems, Nonlinear systems
Abstract: A Dubins vehicle circumnavigation controller is derived based on an analysis of state space dynamics resulting from a state transformation. The controller is designed and analyzed in the state space with two continuous and one discrete variables. We showed that the property of our control strategy can be studied based on 2D phase portraits, including the case when the vehicle turning rate is bounded. As a part of our analysis, we found conditions that need to be satisfied for the controller to work and that can be used for gain scheduling and application of the control to a larger range bound. The results are illustrated by 2D phase portraits and simulations describing the control implementation, which is based only on range measurements.

Keywords: Supervisory control, Automotive, Adaptive systems
Abstract: To deal with possible uncertainties of vehicle longitudinal dynamics especially in complex road condition, a multiple model switching control system for vehicle speed is designed in this paper. Vehicle dynamics from throttle to vehicle speed is linearized about equilibria and a model set consisting of five multiplicative uncertain models is set up to cover the possible range of vehicle dynamics. Based on this model set, the multiple model switching control system for vehicle speed is accomplished based on robust control theory by designing its estimator, switching performance index and logic, robust performance controller set. Simulation results show that this new controller has better adaptive performance than parameter adaptive method and thus better performances of tracking is achieved.

Keywords: Direct adaptive control, Distributed parameter systems, Power systems
Abstract: This paper presents a preliminary study using adaptive control theory to damp the inter-area oscillations in power systems by controlling a wind farm power injection. The power system is modeled as a distributed parameter systems using a first order hyperbolic wave equation, which represents an aggregate model for the system of coupled swing equations. A direct adaptive controller is used to stabilize the power swing in the face of disturbance using power injected from an alternate source like a wind farm.

Keywords: Emerging control applications, Optimization algorithms, Decentralized control
Abstract: Large scale deployment of Plug-in Electric Vehicles (PEVs) in the smart grid environment necessitates the use of appropriate charging control algorithms that handle the additional PEV based load effectively. While ensuring grid stability, these PEV charging control algorithms must ensure that the available energy is delivered to where it is needed the most. In this paper, we explore the question of efficient allocation of energy (charging rates and schedules) to PEVs by the aggregator (utility) through an auction mechanism. Recognizing the practical limitations of the Vickrey-Clark-Groves (VCG) mechanism which would be natural to apply in this context, we investigate two practical mechanisms that can be viewed as extensions of second price auction mechanisms, and have limited message (bid) complexity. In the first mechanism, the multi-level second price (MSP), each PEV agent submits a number of price bids, one for each of a given set of energy levels (charge quantities). In the second mechanism, the progressive second price (PSP), the PEV agents submit a two-dimensional bid indicating the price as well as the desired energy quantity. Taking into account differences across PEV-owners in terms of their willingness-to-pay values and charging time constraints, we analyze the social optimality and incentive compatibility properties of the two auction mechanisms.

Keywords: Power systems, Modeling and simulation, Emerging control applications
Abstract: Load forecasting of energy demand is usually focused on mean values in related statistical models and ignores rare peak events. This paper provides Extreme Value Theory analysis of the peak events in electrical power load demand. It estimates risk of the peak events by combining forecast of the mean with extreme value modeling of distribution tail. The approach is demonstrated for electric load demand data for a US utility. The problem is to find the forecast margins that keep the risk of demand exceeding forecast plus the margin to one event per year. The long tail model of the peak events is more accurate and yields 50% larger margin compared to the normal distribution model. These results show that the long tail behavior of the forecast errors must be taken into account when trying to keep outage risk low.

Keywords: Power systems, Stability of linear systems, Estimation
Abstract: To enable renewable generation to fully contribute to reliable grid operation with higher levels of penetration, it is important that such resources contribute to grid control, including faster time scale active power control that is valuable to small-signal electromechanical stability. However, given the small size and distributed nature of many renewables, it is unrealistic to assume that they will employ the type of centrally coordinated control schemes typical of large, central station synchronous generators. This paper proposes a distributed control design approach that allows each distributed renewable generator or energy storage device to be responsible for improving damping of a single oscillatory electromechanical mode. The consequence is that one may employ a static state feedback design (a classic LQ design is employed here) that requires information of state behavior only within a two-dimension invariant subspace associated with the mode of interest. Using a modal-focused Hautus matrix test of obervability, we develop simple tests to establish grid buses from which a desired mode may be most effectively observed from either local frequency measurement, or from a local measurement plus a single remote phasor measurement unit (PMU) signal. In this framework, the state information required may then be recovered by a Luenberger observer of as little as second order. That is, the local, distributed controller on a given renewable generator or energy storage device uses only one or two measurements, may be as simple as second order, and yet contributes to the system objective of improving modal damping.

Keywords: Power systems
Abstract: The generation reserves play the central role for maintaining generation and consumption balance in the power system. In advance scheduled reserves compensate for forecast error, variability and transmission losses. However, reserves are costly commodity and their amount should be carefully assessed to prevent unnecessary expenses. Currently, the reserve requirements assessment techniques are mainly based on power system operator’s experience and established assumptions, and do not reflect the actual properties of the power system. In this paper a formal mathematical framework for the assessments of the reserve requirements is presented.

Keywords: Power systems, Nonlinear systems, Stability of nonlinear systems
Abstract: When paralleled inverters feed a common load, it is required that the load is shared according to their power ratings. In this paper, the robust droop controller (RDC) proposed in the literature for achieving accurate proportional power sharing for paralleled inverters is implemented in a way to ensure a bounded closed-loop performance. Using non-linear Lyapunov methods, it is shown that the controller structure permits the control input to stay within a predefined range. While maintaining the main theory of the RDC, the proposed bounded droop controller (BDC) is proven to guarantee the stability of the closed-loop system for the general load case given in the generalized dissipative Hamiltonian form, which can describe both linear and non-linear load dynamics. Extended simulation results for two single-phase inverters operated in parallel are presented to verify the effectiveness of the BDC for both a linear and a non-linear load case scenario.

Keywords: Agents-based systems, Optimization, Process control
Abstract: We propose a hybrid method integrating agent-based modeling and heuristic tree search to solve complex batch scheduling problems. Agent-based modeling describes the batch process and constructs a feasible schedule. To overcome myopic decisions of agents, the agent-based simulation is embedded into a heuristic search algorithm. The heuristic algorithm partially explores the solution space generated by the agent-based simulation. Because global information of the objective function value is used in the search algorithm, the schedule performance is improved. As an efficient scheduling algorithm, the hybrid method is applicable to large-scale complex industrial scheduling problems. Its performance is demonstrated by a complex case study from The Dow Chemical Company.

Keywords: Agents-based systems, Autonomous systems, Cooperative control
Abstract: In this paper we propose a novel protocol to solve the consensus on the median value problem, i.e., the decentralized agreement problem for networked multi-agent systems where the quantity of interest is the median value as opposed to the average value of the agents' states. The median value is a statistical measure particularly robust to the existence of outlier agents which are a significant issue in large scale averaging networks. The proposed protocol achieves consensus on the median value in finite time by exploiting a discontinuous local interaction rule.

Keywords: Agents-based systems, Stochastic systems
Abstract: We study a dynamic vehicle routing problem for moving targets. Our setup is as follows: (i) targets appear uniformly distributed on a unit disk via a temporal Poisson process and move radially outward with a constant speed, (ii) a single vehicle with speed greater than that of the targets aims to intercepts them before they escape the disk. With the goal of maximizing the fraction of captured targets in the steady state, we propose three policies: Capturable Nearest Neighbor (CNN), Sector Wise (SW) and Stay Near Boundary (SNB) policy. We derive lower bounds on the fraction of targets captured by the CNN, SW and SNB policies. The CNN policy is shown to be optimal for arrival rate below a critical value. For arrival rates above another critical value, the fraction of targets captured by the SW policy is more than that of the CNN policy. Finally, the SNB policy is within a constant factor of optimal in the limiting regime of low target speed and high arrival rate.

Keywords: Agents-based systems
Abstract: This work investigates multi-vehicle systems in which vehicles compete, among themselves, to capture targets. In line with previous formulations, vehicles have minimal sensing capabilities and limited prior knowledge of target locations. That is, vehicles must actively scour the workspace to find targets. However, the games featured herein have the distinguishing feature of unfolding in dynamic environments whereby targets repopulate the workspace over time. For a variety of such games, we characterize persistent search strategies that enable vehicles to plan trajectories that, in the long-run, prove efficient in the face of inter-vehicle competition and an ever-changing world.

Keywords: Agents-based systems, Optimization algorithms
Abstract: In this paper, we consider a dynamic coverage problem where a team of mobile robots needs to cover a set of time-varying Points of Interest (POIs), while maintaining their connectivity to a remote station in a realistic communication environment. Our goal is to design the motion and communication strategies of the robots such that they periodically visit the POIs, communicate the gathered information to a remote station, minimize their total energy costs, including motion, sensing and communication energy, and satisfy other system constraints. In our previous work [1], we have shown how to pose this problem as a Mixed Integer Linear Program (MILP), which is computationally expensive. In this paper, we focus on designing a considerably more computationally-efficient heuristic approach to tackle this problem. More specifically, we propose to utilize the space-filling curves to efficiently assign the POIs and plan the trajectories of the robots. Under certain conditions, we mathematically show that our heuristic approach is at most a constant factor away from the global optimum. Our simulation results then confirm that our approach is considerably faster than solving the MILP, especially when the dimension of the problem is high. They further show that the energy consumption of our approach is only around 26% more than the optimum.

Keywords: Agents-based systems, Decentralized control, Learning
Abstract: In this paper, we provide a theoretical analysis of log-linear learning algorithm that can be used for studying decision processes and solving control problems related to potential games. So far, for this algorithm the convergence of collective behavior to some state in a potential game has been proven to be specified by a chosen parameter and does not imply the convergence in probability to potential function maximizers. Tending the parameter and time to infinity simultaneously, we investigate the probabilistic convergence of joint actions based on the log-linear learning algorithm. We formulate conditions under which the convergence does not take place at all. Nevertheless, we explain how the parameter and utility functions should be designed to guarantee the probabilistic convergence of system behavior with some stationary distribution. Moreover, for such a setup in potential games the stable states, i.e. those having positive probability in the limit stationary distribution, are from the set of Nash equilibria and maximize the potential function.

Keywords: Control applications, PID control, Mechatronics
Abstract: To provide more tuning freedom in the trade-off between overshoot and settling times of PID controlled stages, a PID controller with a fractional order integrator is studied. The fractional order integrator has two parameters dependent on the order lambda. Namely, the corner frequency w_i(lambda) and the gain k_p(lambda). Both parameters are designed such that robust stability is maintained independent on the choice of lambda. It will be shown that the fractional order integrator can be approximated by a low-order loop shaping filter. As such, insights from fractional order control can be used by the control engineer to aid his tuning tasks. The controller is tested on a wafer stage system.

Keywords: Mechatronics, Robust control, Identification
Abstract: The ongoing need for miniaturization and an increase of throughput in IC-manufacturing is obstructed by performance limitations in motion control of nano-positioning wafer stages. These limitations are imposed by flexible dynamical behavior, associated with structural deformations of the nano-positioning stages. The aim of this research is to investigate limits on achievable performance in a conventional control configuration and to mitigate these limits through the use of additional actuators and sensors. To this end, a systematic framework for control design using additional actuators and sensors in the generalized plant configuration is presented, which leads to a well-posed mathcal{H}_{infty}-control optimization problem that extends conventional design approaches in a natural way and exploits physical insight to address structural deformations in weighting filter design. Through an experimental confrontation of the design framework with a prototype next-generation nano-positioning motion system, successful performance enhancement beyond the conventional limits is demonstrated.

Keywords: Mechatronics
Abstract: Repetitive Control (RC) is a popular technique for tracking periodic signals and rejecting periodic disturbances. Repetitive control achieves accurate tracking of periodic trajectories by incorporating a periodic signal generator within the feedback loop. The periodic signal generator provides an infinite loop-gain at the harmonic frequencies of the reference signal. However, this scheme cannot be used in isolation due to challenges with stability and robustness. The stability and robustness can be improved by incorporating appropriate filters. However, there is a trade-off between robustness and tracking performance. The current state-of-the art is to implement plant inversion and include phase compensators to improve the high- frequency tracking performance. In this work, a RC controller is combined with a non-causal FIR filter to improve the tracking performance without the requirement for phase compensators or plant inversion. The performance of the proposed RC design is demonstrated on a piezoelectric positioner.

Keywords: Nonlinear systems, Nano systems, Manufacturing systems
Abstract: High-precision scan stages are used for fabrication of integrated circuits, liquid crystal displays and so on. To fabricate such precise devices, not only stages position but also stages attitude needs to be controlled rapidly and precisely. In this paper, an experimental 6-degree-of-freedom (6-DOF) high-precision stage with a novel 6-DOF air bearing called ``gravity canceller'' is designed and fabricated. The 6-DOF stage consists of a fine stage and a coarse stage. The gravity canceller compensates for the fine stage's gravity and supports the fine stage without friction. This structure enables us to reduce heat which is generated close to the fine stage. For a 6-DOF control problem, attitude control is as important as translational control. Rotational motion, however, has nonlinearity and coupling arising from dynamics and kinematics which could degrade the attitude control performance. Therefore, in our past paper, our research group proposed a multi-input multi-output nonlinear feedforward attitude controller to compensate such problems. Experiments were performed to verify the effectiveness of the attitude controller by using the new experimental 6-DOF stage.

Keywords: Mechatronics, Nano systems, Robust control
Abstract: In this paper, a novel X-Y parallel piezoelectric-actuator driven nanopositioner is studied from the perspectives of design optimization, dynamical modeling, as well as controller design for high precision positioning. FEM (Finite Element Method) and dynamical modeling are provided to analyze the mechatronic structure of the proposed two dimensional nano-stage, where the system model including hysteresis loop is derived analytically and further verified experimentally. A robust control architecture incorporating an H_infty controller and an anti-windup compensator is then developed to deal with the hysteresis and saturation nonlinearities of the piezoelectric actuators. Real time experiments on the positioning of the nano-stage demonstrate good robustness, tracking performance and recovery speed in the presence of saturation.

Keywords: Nano systems, Mechatronics, Numerical algorithms
Abstract: Continuous development of atomic force microscopy and its applications is not always supported by a rigorous theoretical treatment of the related phenomena. A proper understanding of the behavior of microscopic probes and their response to tip-sample force interactions are crucial for a microscope control, optimization of experimental procedures and rational data analysis in the frameworks of quantitative models. Several issues of the probe dynamics and instrument functionality are addressed with a rigorous theoretical analysis of the thermal tune method, which is presented in this paper. It covers theoretical derivation of the method, development of stable convergent algorithms, different averaging techniques, and implementation with high-speed acquisition or a heterodyne technique for low speed acquisition. The developed thermal tune procedure allows for precise determination of the probe resonant frequency, quality factor and spring constant. It was verified practically with AFM probes operating in different environments. It is also shown that this method is applicable for measurements of the microscope functional parameters such as optical deflection noise and inverse optical sensitivity.

Keywords: Distributed parameter systems, Materials processing
Abstract: In [1], the authors introduced a novel control law for the single-phase Stefan problem, a nonlinear partial differential equation, with the eventual goal of applying the result to control of cooling in continuous steel casting. In this paper, the previously published method of controlling the Stefan problem is extended in two key ways that improve the fidelity of the nonlinear PDE as a model of the temperature and solidification of continuous steel casters. First, a non-symmetric temperature distribution is allowed, in which separate Neumann boundary control is applied at either surface. Two convergent control laws are compared in simulation, with one converging significantly faster. Second, saturation of the Neumann boundary control input is considered. Saturation is a significant concern in the actual process. The enthalpy-based algorithm still allows for convergence under saturation. However, as one would expect, severity of the constraints can affect the convergence rate.

Keywords: Distributed parameter systems, Networked control systems, Fault detection/accomodation
Abstract: This work presents a methodology for the integrated identification and accommodation of control actuator faults in spatially distributed systems controlled over a resource-limited communication network. A finite-dimensional model-based networked controller that enforces closed-loop stability using minimal sensor-controller communication is initially designed, and an explicit characterization of the networked closed-loop stability region is obtained. Fault identification is carried out using a moving-horizon least-squares parameter estimation scheme embedded in the sensors to estimate on-line the size of the fault using the sampled state and input data. Once the fault is identified and its magnitude estimated and communicated to the controller, a number of possible stability-preserving fault accommodation strategies are devised, including updating the post-fault control model, adjusting the controller design parameters, or a combination of both. The selection of the appropriate accommodation strategy is made on the basis of the estimated fault magnitude and the characterization of the networked closed-loop stability region. Finally, the proposed methodology is illustrated using a representative diffusion-reaction process example.

Keywords: Distributed parameter systems, Variable-structure/sliding-mode control, Backstepping
Abstract: In this paper, sliding mode control approach is used to stabilize a 2times2 system of first-order linear hyperbolic PDEs subject to boundary input disturbance. Disturbance rejection is achieved, and with the designed first-order sliding mode controller, the resulting closed-loop system admits a unique (mild) solution without chattering. Convergence to the chosen infinite-dimensional sliding surface of state trajectories takes place in a finite time. Then on the sliding surface, the system is exponentially stable with a decay rate depending on the spatially varying system coefficients. A simulation example is presented to illustrate the effectiveness and performance of sliding mode control method.

Keywords: Emerging control applications, Reduced order modeling, Predictive control for nonlinear systems
Abstract: Presented is a preliminary investigation into the modeling, active control design, and simulation of a vertical axis pendulum wave energy converter, or vertical axis PWEC. Seeking to leverage their promising potential for energy extraction, an active control strategy for the PWEC pendulum dynamics is developed such that net electric power production is increased. Equations of motion for a generic PWEC power take off are developed. Finally, an active control strategy is developed, based on optimal and model predictive control theory.

Simulations of the generic PWEC within the irregular wave environment representative of the Oregon coast oceans, both with and without the active control engaged are given. Comparisons of the simulations indicate strong increases in net PWEC electric power generation by actively controlling the PWEC pendulum's dynamics. Future pathways for active control development and PWEC advancement are then proposed.

Keywords: Estimation, Computational methods, Distributed parameter systems
Abstract: We develop a stochastic finite element based scheme for constructing a likelihood function for the Bayesian estimation of parameters in a distributed parameter model for thin film layered organic photovoltaic cells. The scheme is based on a distributed parameter model for the propagation of the optical wave through the various layers of the cell and its conversion to an electrical current in the active layers of the device. The model consists of three components: a frequency domain wave equation based propagation model for the optical wave through the various transparent and active layers, a diffusion equation describing the dynamics of exciton density in the active layers of the device, and an output equation that describes the external quantum efficiency (EQE) of the cell. There are two parameters in the diffusion equation that have to be estimated based on measurements of EQE: the exciton diffusion length and half-life or lifetime. In this paper, a stochastic finite element based scheme that yields a probability density function (pdf) for EQE as a function of the uncertainty in exciton diffusion length and lifetime are developed. The ultimate goal is to use this pdf to construct a likelihood function as part of a Bayesian estimation scheme for the exciton diffusion length and half-life.. Numerical results involving data from an actual device are presented and discussed.

Keywords: Smart structures, Distributed parameter systems, Variational methods
Abstract: Models for piezoelectric beams using Euler-Bernoulli small displacement theory predict the dynamics of slender beams at the low frequency accurately but are insufficient for beams vibrating at high frequencies or beams with low length-to-width aspect ratios. A more thorough model that includes the effects of rotational inertia and shear strain, Mindlin-Timoshenko small displacement theory, is needed to predict the dynamics more accurately for these cases. Moreover, existing models ignore the magnetic effects since the magnetic effects are relatively small. However, it was shown recently [10] that these effects can substantially change the controllability and stabilizability properties of even a single piezoelectric beam. In this paper, we use a variational approach to derive models that include magnetic effects for an elastic beam with two piezoelectric patches actuated by different voltage sources. Both Euler-Bernoulli and Mindlin-Timoshenko small displacement theories are considered. Due to the magnetic effects, the equations are quite different from the standard equations.

Keywords: Building and facility automation, Identification, Modeling and simulation
Abstract: This paper presents the identification of a lumped thermal model for one floor of a large office building consisting of partitioned cubicles and conference rooms. The space is instrumented with a large number of interior temperature sensors as well as supply-air temperature and flow rate sensors. Roof top solar radiation data is also available. The first step in the model identification process is to cluster the interior temperature measurements into zones. The zones form a thermal connectivity graph based on the physical location of the clusters. Each zone corresponds to a node in the graph with a corresponding thermal capacitance. The zones connect to adjacent zones and to the ambient via thermal resistances. Due to large windows on two walls and the ceiling, the model also needs to account for the radiant heat input into the space. We identify thermal capacitance, thermal resistance, and radiant heat transfer coefficients in the model by matching the predicted temperature trajectory with the measured data. As validation, the model shows reasonable prediction of temperatures on dates not used for identification. Using the identified model, we also consider the temperature control problem with real-world ambient temperature and solar data. By using our previously reported passivity-based temperature controller, we achieve tighter temperature regulation while consuming less energy as compared with the existing controller.

Keywords: Building and facility automation, Markov processes, Control applications
Abstract: This paper presents a model predictive control (MPC) technique for building heating, ventilation, and air conditioning (HVAC) systems. It incorporates the building's thermal dynamics, local weather predictions, and a stochastic occupancy model to reduce energy consumption while maintaining occupant comfort. Using approximate dynamic programming and a cost function weighted by expected occupancy, the scheme extends the capability of conventional model predictive control by pre-conditioning thermal zones before occupancy begins and reducing conditioning before occupancy ends. The resulting control law may be synthesized step-wise using an on-line optimization or may be periodically synthesized off-line and downloaded into an embedded controller. Simulation results demonstrate the efficacy of both approaches.

Keywords: Building and facility automation, Modeling and simulation, Predictive control for linear systems
Abstract: Model predictive control (MPC) is a promising alternative in building control with the potential to improve energy efficiency and comfort and to enable demand response capabilities. Creating an accurate building model that is simple enough to allow the resulting MPC problem to be tractable is a challenging but crucial task in the control development. In this paper we introduce the Building Resistance-Capacitance Modeling (BRCM) Matlab Toolbox that facilitates the physical modeling of buildings for MPC. The Toolbox provides a means for the fast generation of (bi-)linear resistance-capacitance type models from basic building geometry, construction and systems data. Moreover, it supports the generation of the corresponding potentially time-varying costs and constraints. The Toolbox is based on previously validated modeling principles. In a case study a BRCM model was automatically generated from an EnergyPlus input data file and its predictive capabilities were compared to the EnergyPlus model. The Toolbox itself, the details of the modeling and the documentation can be found at www.brcm.ethz.ch.

Keywords: Identification, Grey-box modeling, Building and facility automation
Abstract: As energy efficient model-based controllers gain acceptance in the building domain, the problem of identifying accurate building models for control becomes even more important. Current literature provides analysis on the identifiability of building models. However, in practice, building models can be too large and too complex to properly identify parameter estimates. Towards that end, we present a strategy to decompose large building models into smaller building zone models so that these zone models can be individually identified. We find that the identifiability of zone models imply the identifiability of the building model, and we outline a decentralized approach to identify large building models. Finally, we demonstrate this approach with a simulated building example.

Keywords: Optimization, Control applications, Power systems
Abstract: In recent years, a number of extremum seeking algorithms have been proposed. While each approach aims to estimate the gradient of a performance metric in realtime and steer inputs to values that optimize the metric, the way in which each method accomplishes this goal can have practical implications that depend on the application. In this paper, we compare the performance of traditional perturbation-based extremum seeking to time-varying extremum seeking in the context of optimizing the energy efficiency of a vapor compression system. In order to benchmark these algorithms, we simulate their performance using a moving-boundary model of a vapor compression machine that has been tuned and calibrated to data gathered from a multi- split style room air conditioner operating in cooling mode. We show that while perturbation-based extremum seeking appears simplest to tune, some challenging minima are not obtained. Also, we find that time-varying extremum seeking converges faster and more reliably than the other method tested.

Keywords: Building and facility automation, Predictive control for nonlinear systems, Power systems
Abstract: Commercial buildings have inherent flexibility in how their HVAC systems consume electricity. We investigate how to take advantage of this flexibility. We first propose a means to define and quantify the flexibility of a commercial building. We then propose a contractual framework that could be used by the building operator and the utility to declare flexibility on the one side and reward structure on the other side. We then design a control mechanism for the building to decide its flexibility for the next contractual period to maximize the reward, given the contractual framework. Finally, we perform at-scale experiments to demonstrate the feasibility of the proposed algorithm.

Keywords: Observers for linear systems, Optimization, Distributed parameter systems
Abstract: Motivated by the design of observers with good performance and robustness, the problem of estimating the state of a linear time-invariant plant in a distributed fashion, over a graph, is considered. By attaching to each node a linear observer and defining an innovation term that employs information received from neighbors, we propose a distributed state observer that satisfies a pre-specified rate of convergence and has optimized robustness to measurement noise. The convergence rate and the robustness to measurement noise of the proposed observer are characterized in terms of KL bounds as well as in terms of (nonlinear and linear) optimization problems. Moreover, conditions on the plant for which the proposed observer has an H∞ gain from noise to local estimate that is smaller than that of a single Luenberger observer is given. The properties of the proposed distributed state observer are shown analytically and validated numerically.

Keywords: Estimation, Filtering
Abstract: The multi-Bernoulli (MB) filter is a new attractive approach for multi-target filtering in the presence of clutter and detection uncertainty.However, the computational complexity grows as clutter density increases. The clutter measurements may also degrade the filtering accuracy.To eliminate the clutter measurements and reduce the computational complexity, gating technique for the Gaussian mixture MB (GM-MB) filter is proposed in this paper.Gating technique is designed for each hypothesized track to reduce the number of measurements by discarding most clutter measurements.Simulation results demonstrate the proposed approach can improve the real-time performance without the degradation of filtering performance.

Keywords: Estimation, Uncertain systems, Kalman filtering
Abstract: In this work, a high dimension SLAM simulation is designed to test a Gaussian mixture model based SLAM approach. The Gaussian mixture model approach replaces the particles in FastSLAM with individual Gaussian. Furthermore, sparse grid quadrature is used to propagate the mean and covariance of the Gaussian mixture components. In addition, the high dimensional state is partitioned into linear and nonlinear terms, reducing the state that is maintained with the Gaussian mixture. The requirement is an additional extended Kalman filter for each component. The experimental setup is described, and preliminary results show an accuracy increase when compared to the FastSLAM 2.0 algorithm.

Keywords: Estimation, Filtering, Optimization
Abstract: This paper considers the problem of synthesizing H_2 filters subject to sparsity constraints on their structure, that is, constraints on which inputs can be used when computing the estimated value of a given output. The main result of the paper shows that, contrary to the sparse controller case,the necessary and sufficient condition for the existence of filters satisfying a given sparsity pattern is related to the existence of solutions to a finite set of linear equations. Further, when the problem is feasible, then it can be solved using convex optimization and the objective function exhibits a ``separation" like structure that clearly indicates the cost of sparsity.

Keywords: Estimation, Fault-tolerant systems, Stochastic systems
Abstract: This work addresses the design of resilient estimators for stochastic systems. To this end, we introduce a minimum mean-squared error resilient (MMSE-R) estimator whose conditional mean squared error from the state remains finitely bounded and is independent of additive measurement attacks. An implementation of the MMSE-R estimator is presented and is shown as the solution of a semidefinite programming problem, which can be implemented efficiently using convex optimization techniques. The MMSE-R strategy is evaluated against other competing strategies representing other estimation approaches in the presence of small and large measurement attacks. The results indicate that the MMSE-R estimator significantly outperforms (in terms of mean-squared error) other realizable resilient (and non-resilient) estimators.

Keywords: Markov processes, Optimal control, Control applications
Abstract: We propose a Markov decision based dynamic programming method to manipulate the self-assembly of a quadrupole colloidal system for grain-boundary-free two-dimensional crystals. To construct the optimal control policy, we developed a Markov chain model, based on information extracted from a Langevin dynamics simulation model, which originated from a more complicated Brownian dynamics model. An infinite-horizon Markov decision process is defined, and the optimal control policy is solved with dynamic programming using policy iteration. Both the Markov chain Monte Carlo and the Langevin dynamics simulation results demonstrate that the control strategy proposed in this paper is able to significantly accelerate the crystallization of a SiO2 colloidal self-assembly process for a grain-boundary-free, highly ordered crystal. Future work will focus on implementation of the control policy on the Brownian dynamics simulation and the experiments.

Keywords: Markov processes, Pattern recognition and classification, Fault detection/accomodation
Abstract: This paper presents an approach for symbolic analysis of time-series data from a dynamical system perspective that uses Markov modeling of symbol sequences. A key aspect of this approach is the selection of relevant histories or depth of the symbol sequence to capture the evolution of the observed dynamical system in its phase-space. An approach based on the spectral properties of the one-step transition matrix is proposed. A key advantage of this approach compared to the state-of-the-art is that it does not require several passes through the time-series data to {it search} for the optimal model given some metric. The proposed approach makes use of the decay-rate of conditional influence of the current symbol to the n-step future of the dynamical system. Using a bound on this decay rate, the optimal depth can be computed in exactly one pass though the time-series data. The effectiveness of the proposed methodology is demonstrated on a known low-dimensional chaotic system and the efficacy of the approach for anomaly detection is demonstrated.

Keywords: Pattern recognition and classification, Machine learning, Wireless
Abstract: This paper addresses sensor network-based surveillance of target detection and target type & motion classification. The classification performance is often limited due to high rates of false alarm and misclassification, often because of inadequacies of feature extraction from (possibly noisy) sensor data and subsequent pattern classification over the network. A feature extraction algorithm, called symbolic dynamic filtering (SDF), is investigated for solving the above target detection & classification problem. In SDF, the sensor time series data are symbolized to construct probabilistic finite state automata (PFSA) and low-dimensional feature vectors are obtained from the resulting PFSA. In this paper, the performance of SDF is compared with two commonly used feature extractors (i.e., Cepstrum and principal component analysis (PCA)) while each of these feature extractors are executed in conjunction with three well known pattern classifiers, namely, k-nearest neighbor (k-NN), support vector machine (SVM), and sparse representation classification (SRC), for target detection & classification by passive sonar sensor systems. Results of numerical simulation are presented based on a dynamic model of target maneuvering in the ocean environment. These results show that SDF has a consistently superior performance for all three tasks of target detection, target type classification, and target motion classification.This paper addresses sensor network-based surveillance of target detection and target type & motion classification. The performance of target detection and classification could be compromised (e.g., due to high rates of false alarm and misclassification), because of inadequacies of feature extraction from (possibly noisy) sensor data and subsequent pattern classification over the network. A feature extraction algorithm, called symbolic dynamic filtering (SDF), is investigated for solving the target detection & classification problem. In this paper, the performance of SDF is compared with two commonly used feature extractors, namely, Cepstrum and principal component analysis (PCA)). Each of these three feature extractors is executed in conjunction with three wellknown pattern classifiers, namely, k-nearest neigh

Keywords: Pattern recognition and classification, Process control, Control system architecture
Abstract: In this paper, a data-driven control approach for batch processes based on feature extraction is proposed. In this strategy, a model of the system dynamics is not needed and the issue of adapting to different operating conditions is recast into a simple controller design problem. Throughout the paper, the proposed algorithm will be described in detail with the help of an experimental gravimetric blender example.

Keywords: Identification, Estimation, Hybrid systems
Abstract: We propose a Maximum Likelihood-based method that combines the Expectation Maximization algorithm with Sequential Monte Carlo methods to estimate fixed parameters and transition probabilities for a general class of nonlinear jump Markov systems in state space form. This method is an extension to a previous method originally proposed by T. B. Schon, A. Wills, and B. Ninness for identifying the parameters of a class of nonlinear systems that are not dependent on a Markov chain. In this work, we detail an extension of this method to jump Markov systems and illustrate it through its application to identifying the parameters of the logistic map driven by white Gaussian noise with variance governed by a discrete Markov process.

Keywords: Modeling and simulation, Markov processes, Computational methods
Abstract: A technique to approximate heat diffusion on Riemannian manifolds is presented. We provide a numerical way to approximate the solution to the heat equation by using the idea of random walks of particles, governed by a continuous-time Markov chain, where the transition rates of the Markov chain are characterized by the distances between nodes on a given grid with non-equally placed nodes. The emphasis lies on the fact that nodes do not need to be distributed equidistant from each other, since such a regular grid is not effective on many manifolds, where some parts of the manifold require less nodes than others due to curvature. In this paper we show how to characterize the Markov chain for a given grid in order to build a framework for the numerical approximation of the solution to the heat equation on Riemannian manifolds. This framework approximates the Laplace-Beltrami operator which is used on such manifolds. Furthermore, we discuss advantages of this technique and provide examples and simulations of our results.

Keywords: Nonlinear systems, Decentralized control, Power systems
Abstract: In this paper, we design a distributed dynamic feedback controller that implements real-time economic optimization in power networks with tree topology. We consider the coupling between the dynamics of the power network and the appropriately formulated optimization problem in the real-time market, i.e., when the time-scale separation between markets and network dynamics fades. The design methodology is motivated by optimization decomposition methods in that the controller is derived from a primal-dual decomposition approach. We then prove the asymptotic stability of the overall (closed-loop) system in a scalable fashion. The performance of the controller is illustrated by numerical investigations.

Keywords: Nonlinear systems, Modeling and simulation, Aerospace
Abstract: Aeroelastic flutter is a dynamic instability of fluid-structural system in which the structure exhibits a sustained, often diverging oscillation. Flutter behavior is self-feeding and destructive. Nonlinearities such as freeplay in rigid-body rotational stiffness of the structural system can have an effect on the onset of flutter and its amplitude. In particular there is experimental evidence that as the amount of freeplay increases, the freestream velocity at which the flutter instability occurs decreases. In this paper, we develop a modeling framework that allows us to predict this dependence of flutter velocity on the freeplay parameter. We model the airfoil system with freeplay nonlinearity as a feedback interconnection of linear system and sector bounded nonlinearity. Freeplay in stiffness is practically approximated as a hyperbola nonlinearity. Eigenvalue analysis at equilibrium points is used to predict onset of flutter and characterize a Hopf bifurcation of the system from stable to limit cycle behavior. Spectral analysis us used to characterize the limit cycle behavior. This analysis indicates the flutter onset velocity to be a function of freeplay region length. Follow-on research correlating recently obtained wind tunnel results to a three-dimensional extension of the model is outlined.

Keywords: Output feedback, Uncertain systems, Robust adaptive control
Abstract: A general class of uncertain nonlinear systems with uncertain input unmodeled dynamics is considered. The system structure includes a core nominal subsystem of triangular structure with additive uncertain nonlinear functions and coupled with a set of uncertain nonlinear appended dynamics as well as uncertain dynamic input nonlinearities. The dynamic input nonlinearities include an uncertain non-affine function and a dynamic perturbation through an uncertain input unmodeled dynamics subsystem. The input unmodeled dynamics subsystem is coupled with the entire system state including the unmeasured state of the uncertain nonlinear appended dynamics coupled with the nominal triangular system. The control design is based on dual controller/observer dynamic high-gain scaling with an additional dynamic scaling based on a singular-perturbation-like redesign to address the non-affine and uncertain nature of the input appearance into the system dynamics. The proposed approach yields a global robust adaptive output-feedback control design that is robust to the dynamic input uncertainties and to uncertain nonlinear functions allowed throughout the system structure.

Keywords: Nonlinear systems, Stability of nonlinear systems
Abstract: The consideration of nonsmooth Lyapunov functions for proving stability of feedback discontinuous systems is an important extension to classical stability theory since there exist nonsmooth dynamical systems whose equilibria cannot be proved to be stable using standard continuously differentiable Lyapunov function theory. For dynamical systems with continuously differentiable flows, the concept of smooth control Lyapunov functions was developed by Artstein to show the existence of a feedback stabilizing controller. A constructive feedback control law based on a universal construction of smooth control Lyapunov functions was given by Sontag. Even though a stabilizing continuous feedback controller guarantees the existence of a smooth control Lyapunov function, many systems that possess smooth control Lyapunov functions do not necessarily admit a continuous stabilizing feedback controller. However, the existence of a control Lyapunov function allows for the design of a stabilizing feedback controller that admits Filippov and Krasovskii closed-loop system solutions. In this paper, we develop a constructive feedback control law for discontinuous dynamical systems based on the existence of a nonsmooth control Lyapunov function defined in the sense of generalized Clarke gradients and set-valued Lie derivatives.

Keywords: Nonlinear systems, Optimization, Robust adaptive control
Abstract: A novel nonlinear control scheme for maximum power seeking in wind turbine energy systems is presented in this paper. In the proposed control scheme, the wind turbine power coefficient is estimated using a Lyapunov-based adaptive control technique. The power coefficient estimation procedure differs from previous estimation strategies. The estimated power coefficient is used to determine a desired turbine rotor speed which is based on an estimate of the power coefficient gradient with respect to rotor speed. The desired rotor speed moves the turbine operation in the direction of increasing power coefficient and a PI controller causes the turbine rotor speed to track the desired rotor speed. The presented extremum seeking procedure differs markedly from the well-known perturb and observe scheme and does not require the addition of a dither signal. The NREL FAST numerical results are presented to demonstrate the validity and robustness of the developed control scheme.

Keywords: Autonomous systems, Flight control, Aerospace
Abstract: This paper proposes a method for predicting the point at which a simple lateral collision avoidance manoeuvre fails. It starts by defining the kinematic failure boundary for a range of conflict geometries and velocities. This relies on the assumption that the ownship aircraft is able to turn instantaneously. The dynamics of the ownship aircraft are then introduced in the form of a constant rate turn model. With knowledge of the kinematic boundary, two optimisation algorithms are used to estimate the location of the real failure boundary. A higher fidelity simulation environment is used to compare the boundary predictions. The shape of the failure boundary is found to be heavily connected to the kinematic boundary prediction. Some encounters where the ownship aircraft is travelling slower than the intruder were found to have large failure boundaries. The optimisation method is shown to perform well, and with alterations to the turn model, its accuracy can be improved. The paper finishes by demonstrating how the failure boundary is used to determine accurate collision avoidance logic. This is expected to significantly reduce the size and complexity of the verification problem.

Keywords: Autonomous systems, Modeling and simulation, Air traffic management
Abstract: Sense and avoid is the primary technical hurdle to unmanned aircraft system airspace integration. Several sense and avoid system concepts have been proposed, including numerous system architectures and technologies for aircraft surveillance, and threat detection and resolution. However, there is little evidence to support concept refinement, evaluation, requirements development, and validation. This paper introduces a methodology for evaluating, refining, and validating sense and avoid system concepts through flight testing tightly coupled to modeling and simulation safety analysis. This methodology is demonstrated through the analysis and flight testing of an airborne primary radar based sense and avoid system intended for the Department of Homeland Security Predator B variant, a medium altitude, long endurance unmanned aircraft system.

Keywords: Stochastic systems, Air traffic management, Uncertain systems
Abstract: Safe integration of Unmanned Aircraft Systems (UAS) into the National Airspace (NAS) will require the development and fielding of a sense-and avoid (SAA) capability to augment the traditional ``see-and-avoid" regulations of manned aircraft. In this paper, we focus on the problem of correctly predicting an intruder trajectory. Approaches to intruder prediction are typically grouped into three categories: (i) nominal (deterministic), (ii) worst-case, and (iii) probabilistic. Most prediction algorithms envisioned for SAA fall within the probabilistic category. The benefit of a probabilistic approach is that it provides a mechanism to represent unknown variations in the intruder state at future times while also avoiding the overly conservative assumptions inherent in worst-case prediction. The downside of a probabilistic prediction is that it necessitates the construction of a stochastic model that is both useful for computation and accurately represents the ``true" uncertainty in intruder predictions. Markovian structure and time-discretization are very common simplifying assumptions made to satisfy the first goal. Data-driven model tuning is typically used for the latter. However, a model is never exact, and a large quantity of data may be needed to guarantee the approximation accuracy is sufficient.

The primary contribution of this paper is to present a fourth option for intruder prediction that we refer to as ``robust probabilistic prediction." It is meant to address the risk of model mismatch associated with traditional probabilistic predictions. Conceptually, the idea is to specify only those features of the stochastic model that can be justified by data or expert judgment, leaving a full stochastic model underspecified. Typically, one needs a full stochastic model to ``turn the crank" on risk calculations (e.g., probability of Near Mid-Air Collision). However, in robust probabilistic predictions, risk is defined as the worst-case risk over a space of stochastic models. This relaxes the need for ensuring that all elements of the model are correct. We show that a computationally efficient semi-definite program (SDP) can be used for performing the optimization over the space of stochastic models.

Keywords: Vision-based control, Autonomous systems, Flight control
Abstract: The real world feasibility of a self-contained, purely vision based sense and avoid system, required for small unmanned aerial vehicles (UAV) to routinely access the national airspace is investigated in the present paper. No information is exchanged between aircraft, only passive 2-D vision information is available to estimate the encountering traffic. The system is composed of three distinct components: intruder state estimation using Unscented Kalman filter (UKF); collision risk estimation based on probabilistic algorithms; and trajectory re-generation using motion primitives to minimize the expected probability of collision. Since the relative system dynamics are weakly observable special emphasis is made on generating trajectories during the estimation phase to provide persistent excitation. The system is tested on a high fidelity Hardware-in-the-Loop (HIL) simulation platform, where flight control algorithms, scene rendering, image processing and estimation algorithms are implemented individually over a network of computers. A high number of representative encounter scenarios are presented to provide performance measures, including detection time and miss distance of distinctive approaches to assess the applicability of the proposed method.

Keywords: Autonomous systems, Aerospace, Vision-based control
Abstract: This paper is focused on measurement and control schemes for a quadrotor UAV to precisely land on a flat inclined surface without prior knowledge of the surface's orientation. Assuming that the location of the landing site is known, a quadrotor performs a flyover and uses lasers and a CMOS camera to detect the projections of the lasers on the ground plane to measure the relative ground plane angle of the landing site. This information is then used to design an aggressive landing trajectory such that the quadrotor touches down parallel to the landing surface. During the initial phase of the maneuver, a trajectory-tracking controller guides the quadrotor, and as it nears the landing phase, an attitude-tracking controller ensures that the attitude of the quadrotor matches the slope of the landing platform upon touchdown. This approach is illustrated by numerical examples and preliminary experimental results for laser-based surface angle determination.

Keywords: Autonomous systems, Multivehicle systems, Decentralized control
Abstract: In this paper, we present a decentralized trajectory tracking control with collision avoidance for an arbitrarily large group of heterogeneous, nonholonomic vehicles. We consider vehicles with different constant speeds and bounded turning rates (e.g., airplanes), for which their avoidance maneuverability is limited. The proposed controller is divided in two parts: an auxiliary system and a local heading control. The auxiliary system is in charge of guiding the vehicle toward the desired trajectory while guaranteeing collision avoidance with other agents at all times. The local heading control is designed such that the vehicle remains continuously within a bounded, short distance of the auxiliary system. To enforce the convergence of the vehicles to a proximity of their desired trajectories and avoid deadlocks (a common issue within most decentralized navigation methods), an alternative discontinuous control strategy is presented. Finally, a numerical example is provided to illustrate the performance of the proposed control strategy.

Keywords: Optimal control, Power systems, Stochastic systems
Abstract: This paper proposes a method to design an optimal dynamic contract between a principal and an agent, who has the authority to control both the principal’s revenue and an engineered system. The key characteristic of our problem setting is that the principal has very limited information: the principal has no capability to monitor the agent’s control or the state of the engineered system. The agent has perfect observations. With this asymmetry of information, we show that the principal can induce the agent to control both the revenue and the system processes in a way that maximizes the principal’s utility, if the principal offers appropriate real-time and end-time compensation. We reformulate the dynamic contract design problem as a stochastic optimal control of both the engineered system and the agent’s future expected payoff, which can be numerically solved using an associated Hamilton-Jacobi-Bellman equation. The performance and usefulness of the proposed contract are demonstrated with an indirect load control problem.

Keywords: Optimal control, Uncertain systems, Finance
Abstract: Economic problems in the optimal management of strategic resource stockpiles can be rigorously studied and solved by formulating them as optimal control problems in continuous time. Often, the real economic systems and stockpiling scenarios of interest exhibit both stochastic features and input saturation effects. Building on work to account for saturation effects into basic optimal stockpile problems, the following paper solves a basic optimal stockpile model with a stochastic saturation limit as well as a stochastic solution interval.

Keywords: Optimal control, Neural networks, Learning
Abstract: The problem of global optimality analysis of approximate dynamic programming based solutions is investigated in this study. Sufficient conditions for global optimality is obtained without requiring the state penalizing terms in the cost function or the functions representing the dynamics to be convex functions. Afterwards, the theoretical results are confirmed through a qualitative analysis of an example problem.

Keywords: Neural networks, Intelligent systems, Optimal control
Abstract: This paper develops a novel neural network (NN) based finite-horizon approximate optimal control of nonlinear continuous-time systems in affine form when the system dynamics are complete unknown. First an online NN identifier is proposed to learn the dynamics of the nonlinear continuous-time system. Subsequently, a second NN is utilized to learn the time-varying solution, or referred to as value function, of the Hamilton-Jacobi-Bellman (HJB) equation in an online and forward in time manner. Then, by using the estimated time-varying value function from the second NN and control coefficient matrix from the NN identifier, an approximate optimal control input is computed. To handle time varying value function, a NN with constant weights and time-varying activation function is considered and a suitable NN update law is derived based on normalized gradient descent approach. Further, in order to satisfy terminal constraint and ensure stability within the fixed final time, two extra terms, one corresponding to terminal constraint, and the other to stabilize the nonlinear system are added to the novel update law of the second NN. No initial stabilizing control is required. A uniformly ultimately boundedness of the closed-loop system is verified by using standard Lyapunov theory.

Keywords: Optimal control, Autonomous systems, Numerical algorithms
Abstract: This paper considers the planning of collision-free and energy-optimal trajectories for linear dynamical systems with decoupled dynamics. A direct transcription of such a problem generally results in a non-convex problem due to the collision avoidance constraint. In this paper we propose an Alternating Quadratic Programming (AQP) algorithm to deal with the non-convex collision avoidance constraint, and generate a suboptimal solution for the original problem by alternatively solving a number of subproblems. It is proved that the AQP algorithm is guaranteed to converge, and the solution is locally optimal when the boundary of the collision-free region satisfies certain properties. The speed and energy-saving performance of the proposed method are demonstrated by numerical examples.