Keywords:Stochastic optimal control, Control over communications, Stochastic systems Abstract: Randomized control strategies which achieve the Control-Coding Capacity of Linear Quadratic Gaussian Control systems with complete information, are transformed, hierarchically into controller-encoder strategies, which stabilize the control system, encode information signals, and operate at the control-coding capacity. Further, it is shown that among all controllers, encoders and decoders which minimize Mean Square Error (MSE), the conditional mean decoder is optimal and linear controller-encoder-decoders are optimal.

Keywords:Stochastic optimal control, Game theory, Markov processes Abstract: An incentive Stackelberg game for a class of Markov jump linear stochastic systems with multiple leaders and followers is investigated. An incentive structure is developed that allows the leader’s Nash equilibrium to be achieved. In the game, the followers are assumed to behave in two ways under the leader’s incentive strategy set. One involves achieving a Pareto-optimal solution, and the other involves achieving Nash equilibrium. Consequently, it can be verified that irrespective of how the followers behave, they can be induced to achieve the leader’s Nash equilibrium by using a corresponding incentive strategy set. It is shown that the incentive strategy set can be obtained by solving the cross-coupled stochastic algebraic Riccati-type equations. As another important contribution, a novel concept of incentive possibility is proposed for a special case. In order to demonstrate the effectiveness of the proposed scheme, a numerical example is solved.

Keywords:Stochastic optimal control, Optimal control Abstract: In this paper we study value function approximation techniques that are based on the Linear Programming formulation of Approximate Dynamic Programming. We propose a point-wise maximum adaptation of the the Linear Programming formulation, which renders the problem non-linear and non-convex. We show that the proposed formulation is equivalent to the Linear Programming formulation, and we apply a series of approximation steps to develop an iterative algorithm for computing value function approximations. We demonstrate the computational advantages and approximation quality of the proposed algorithm through numerical examples on systems of low and high dimension.

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Keywords:Stochastic optimal control, Intelligent systems, Lyapunov methods Abstract: This paper considers the Quality-of-Service (QoS) aware pricing and scheduling problem in wireless cloud computing, where the service provider provides a set of services to users through wireless communication. In this process, the provider announces a price for each service according to the system state and queue length. By collecting the service demand from users and observing the system state, the provider allocates some resources to serve the demand. Due to the dynamic of both the demand and system state, it is hard for the provider to make price and schedule the resources optimally. This paper first formulates the problem as a mathematical model. Then, it proposes the QoS aware dynamic pricing and scheduling algorithm (QDPSA). The QDPSA algorithm only depends on the current system state and queue length and can maximize the average profit of the operator. Simulations show that we can get better tradeoff between the profit and queue length through setting a control parameter. In addition, our results also demonstrate that the service with higher value of QoS coefficient can achieve shorter queue length, i.e. shorter response time.

Keywords:Stochastic optimal control, Markov processes, Uncertain systems Abstract: We consider the problem of constructing control policies that are robust against distribution errors in the model parameters of Markov decision processes. The Wasserstein metric is used to model the ambiguity set of admissible distributions. We prove the existence and optimality of Markov policies and develop convex optimization-based tools to compute and analyze the policies. Our methods, which are based on the Kantorovich convex relaxation and duality principle, have the following advantages. First, the proposed dual formulation of an associated Bellman equation resolves the infinite dimensionality issue that is inherent in its original formulation when the nominal distribution has a finite support. Second, our duality analysis identifies the structure of a worst-case distribution and provides a simple decentralized method for its construction. Third, a sensitivity analysis tool is developed to quantify the effect of ambiguity set parameters on the performance of distributionally robust policies. The effectiveness of our proposed tools is demonstrated through a human-centered air conditioning problem.

Keywords:Stochastic optimal control, Stochastic systems, Markov processes Abstract: Using a recently established variational representation for asymptotic growth rate of risk-sensitive reward, equivalent dynamic programming and linear programming formulations are recovered for finite state risk-sensitive reward processes in absence of irreducibility.

Keywords:Delay systems;Distributed parameter systems, Stability of nonlinear systems Abstract: We state a characterization of input-to-state stability (ISS) for a broad class of control systems, including time-delay systems, partial differential equations, ordinary differential equations, switched systems etc. Next we specify this result for a general class of nonlinear time-delay systems. We show that in this case some additional refinements are possible. Finally, we throw some light on several important open problems in ISS theory of time-delay systems: whether time-invariant forward complete systems necessarily have bounded reachability sets and whether limit property and uniform limit properties are equivalent for nonlinear time-delay systems.

Keywords:Delay systems;Distributed parameter systems Abstract: This paper presents a prediction-based controller strategy for linear systems subject to a state-dependent state delay and distinct constant input delays. We propose to compute corresponding predictions in cascade and use them in a nominal control law obtained from the input-delays free case. Using transport Partial Differential Equation (PDE) reformulations and backstepping transformations, we show that this control law compensates for the input delays in closed loop and provides nominal exponential stabilization. The mechanisms of this technique are illustrated on the dynamics of the mechanical vibrations in drilling, which has recently been described as a cutting process.

Keywords:Delay systems, Estimation Abstract: This paper presents a new method to design observers for linear time-delay systems with unknown inputs. In contrast to many observers, which normally estimate the system state in an asymptotic fashion, the proposed observer estimates the exact system state in predetermined finite time. The finite time observer proposed is achieved by updating the observer state based on actual and past data of the observer. Simulation results are also presented to illustrate the convergence behavior of the finite-time observer.

Keywords:Delay systems, Identification for control;Automotive control Abstract: In this paper we propose a data-based algorithm to identify parameters in a human car-following model, in order to facilitate the implementation of connected cruise control in real traffic. We first present a four-car experiment where the trajectory of each vehicle is recorded. Using the experimental data we identify the car-following parameters for each driver. Using the mean values of human parameters, we implement an optimal connected cruise controller on the last vehicle in the four-car string. We demonstrate by numerical simulation that the optimal connected vehicle design based on human parameter estimation has much smaller variations in headway and velocity, and acceleration compared with the human driver.

Keywords:Delay systems, Lyapunov methods, Sampled-data control Abstract: In this paper it is shown how to implement digitally a significant class of state feedbacks for nonlinear retarded systems. Sufficient conditions are provided such that the sampled-data controller provides practical stabilization with arbitrarily small final target ball of the origin. The results shown in the paper by the author, titled ''Stability preservation under sampling and approximation of feedbacks for retarded systems'', SIAM SICON, 2016, are here further validated with applications to a population model and to a continuous stirred tank reactor. For ease of presentation, in this conference paper uniform sampling is employed. Moreover, here just the local case is presented, as very frequent in practical applications.

Keywords:Delay systems, Networked control systems, PID control Abstract: This paper studies the delay margin problem of discrete-time, linear time-invariant (LTI) systems. For general LTI plants with multiple unstable poles and nonminimum phase zeros, we employ analytic function interpolation and rational approximation techniques to derive bounds on the delay margin. Readily computable and explicit lower bounds are found by computing the real eigenvalues of a constant matrix, and LTI controllers can be synthesized based on the H-infinity control theory to achieve the bounds. For first-order unstable plants, we also obtain bounds achievable by PID controllers, which are of interest to PID control design and implementation. It is worth noting that unlike its continuous-time counterpart, the discretetime delay margin problem being considered herein constitutes a simultaneous stabilization problem, which is known to be rather difficult. While previous work on the discrete-time delay margin led to negative results, the bounds developed in this paper provide instead a guaranteed range of delays within which the delay plants can be robustly stabilized, and in turn solve the special class of simultaneous stabilization problems in question.

Keywords:Optimization algorithms, Distributed control, Numerical algorithms Abstract: This paper addresses synchronization of Euclidean transformations over graphs. Synchronization in this context, unlike rendezvous or consensus, means that composite transformations over loops in the graph are equal to the identity. Given a set of non-synchronized transformations, the problem at hand is to find a set of synchronized transformations approximating well the non-synchronized transformations. This is formulated as a nonlinear least-squares optimization problem. We present a distributed synchronization algorithm that converges to the optimal solution to an approximation of the optimization problem. This approximation stems from a spectral relaxation of the rotational part on the one hand and from a separation between the rotations and the translations on the other. The method can be used to distributively improve the measurements obtained in sensor networks such as networks of cameras where pairwise relative transformations are measured. The convergence of the method is verified in numerical simulations.

Keywords:Optimization algorithms, Stochastic systems, Markov processes Abstract: Analysis of every algorithm for stochastic optimization seems to require a different convergence proof. It would be desirable to have a unified mathematical framework within which with minimal extra effort, proof of convergence and its rate could be obtained. We present a random monotone operator-based unified convergence analysis framework for iterative algorithms for strongly convex stochastic optimization. The framework offers both versatility and simplicity, and allows for clean and straightforward analysis of many algorithms for stochatic convex minimization, saddle-point problems and variational inequalities. We show convergence of the ran- dom operator to a probabilistic fixed point, and obtain non- asymptotic rates of convergence. The analysis technique relies on a novel stochastic dominance argument.

Keywords:Optimization algorithms, Optimization, Large-scale systems Abstract: This paper proposes a customized Alternating Direction Method of Multipliers (ADMM) algorithm to solve the Rank-Constrained Optimization Problems (RCOPs) with approximate formulations. Here RCOP refers to an optimization problem whose objective and constraints are convex except a (nonconvex) matrix rank constraint. We first present an approximate formulation for the RCOP with high accuracy by selecting an appropriate parameter set. Then a general ADMM frame is employed to solve the approximated problem without requiring singular value decomposition in each subproblem. The new formulation and the customized ADMM algorithm greatly enhance the computational efficiency and scalability. While ADMM has been extensively investigated for convex optimization problems, its convergence property is still open for nonconvex problems. Another contribution of this paper is to prove that the proposed ADMM globally converges to a stationary point of the approximate problem of RCOP. Simulation examples are provided to demonstrate the feasibility and efficiency of the proposed method.

Keywords:Optimization algorithms, Optimization, Linear systems Abstract: This paper studies the interplay between dynamics and statistics of a stochastically driven dynamical system. Motivation is provided by applications in fluid flow modeling and control. In this context, second-order statistics around the mean velocity profile can be obtained, for a subset of variables, from experiments or numerical simulations. The basic idea is to determine a parsimonious perturbation of the generator of the linearized Navier-Stokes equations, together with directions of excitation sources, that can account for the observed statistics. This covariance completion problem is to determine minimum energy and low-rank perturbation of the linearized dynamics to reconcile them with the partially available second-order statistics – such models are valuable as tools for analysis and control purposes. The resulting optimization problem can be cast as a convex semidefinite program (SDP). However, general purpose SDP solvers cannot handle typical problem-sizes that are of interest in fluid flows. We develop customized algorithms that allow handling such covariance completion problems for substantially larger scales. These algorithms exploit the structure of the problem and utilize the method of multipliers and the proximal augmented Lagrangian method.

Keywords:Optimization algorithms, Optimization, Numerical algorithms Abstract: Sequential Quadratic Programming (SQP) is a powerful class of algorithms for solving nonlinear optimization problems. Local convergence of SQP algorithms is guaranteed when the Hessian approximation used in each Quadratic Programming subproblem is close to the true Hessian. However, a good Hessian approximation can be expensive to compute. Low cost Hessian approximations only guarantee local convergence under some assumptions, which are not always satisfied in practice. To address this problem, this paper proposes a simple method to guarantee local convergence for SQP with poor Hessian approximation. The effectiveness of the proposed algorithm is demonstrated in a numerical example.

Keywords:Switched systems, Time-varying systems, LMIs Abstract: This paper addresses the problem of determining static output feedback controllers for stabilizing continuous-time switched linear systems with either dwell time constraints or arbitrary switching. This problem is addressed by searching for a family of homogeneous polynomial Lyapunov functions (HPLFs) parameterized by the sought controller that prove stability for the considered set of switching rules. In order to conduct this search, polynomials are introduced for approximating the matrix exponential and for quantifying the feasibility of the Lyapunov inequalities. It is shown that there exists a stabilizing controller if and only if a condition built solving three convex optimization problems with linear matrix inequalities (LMIs) holds for polynomials of degree sufficiently large.

Keywords:Model/Controller reduction;Delay systems Abstract: A model reduction approach for asymptotically stable linear delay-differential equations is presented in this paper. Specifically, a balancing approach is developed on the basis of energy functionals that provide (bounds on) a measure of energy related to observability and controllability, respectively. The reduced-order model derived in this way is again a delay-differential equation, such that the method is structure preserving. In addition, asymptotic stability is preserved and an a priori bound on the reduction error is derived, providing a measure of accuracy of the reduction. The results are illustrated by means of application on an example.

Keywords:Model/Controller reduction, Linear systems, Optimization algorithms Abstract: This paper addresses two open problems in the area of model reduction for negative imaginary systems. The first one is the H2 model reduction problem for stable negative imaginary systems, and the second one is the mixed H2/H∞ model reduction problem. Sufficient conditions in terms of matrix inequalities are derived for the existence and construction of H2 and mixed H2/H∞ reduced-order negative imaginary systems. Iterative algorithms are provided to solve the matrix inequalities and to minimize the H2 approximation error. Finally, numerical examples are used to demonstrate the effectiveness of the proposed model reduction methods.

Keywords:Model/Controller reduction, Modeling, Optimization algorithms Abstract: A new Model-order Reduction (MoR) procedure, that combines Galerkin projection method with H2-norm minimization technique, is developed for linear continuous-time systems. The proposed algorithm, stated as the solution of a set of Linear-Matrix-Inequality (LMI) conditions, is subsequently utilized in MoR of nonlinear systems with stability preservation. The developed algorithms are dedicated to stable MIMO linear systems and stable MIMO nonlinear systems having repetitive nonlinearities. The obtained results are compared to the classical balanced truncation algorithm, the Hankel MoR method, and the recent incremental balanced truncation procedure. The designs are illustrated through many examples including a case of a nonlinear high-order electrical circuit.

Keywords:Model/Controller reduction, Predictive control for nonlinear systems, Optimal control Abstract: We present a new application of proper orthogonal decomposition (POD) to optimal control. By restricting the Lagrangian of an optimal control problem to a suitable affine subspace, we can achieve a reduction in computational cost leading to faster turnaround times with minimal degradation in controller performance. An explicit algorithm for nonlinear model predictive control (NMPC) reduction using POD is presented along with some initial error analysis. To the best of our knowledge, this is the first time such an approach has been presented. We applied this approach to the control of a vehicle during a double lane change maneuver using NMPC and achieved 2 times faster turnaround times with excellent controller performance. This reduction approach for the development of real-time optimal controls is very promising and introduces some new research directions.

Keywords:Model/Controller reduction, Stability of nonlinear systems Abstract: In this letter we investigate a class of slow-fast systems for which the classical model order reduction technique based on singular perturbations does not apply due to the lack of Normally Hyperbolic critical manifold. We show, however, that there exists a class of slow-fast systems that after a well-defined change of coordinates (blow up) have a Normally Hyperbolic critical manifold. This allows the use of model order reduction techniques and to qualitatively describe the dynamics from auxiliary reduced models even in the neighborhood of a non-hyperbolic point. As an important consequence of the model order reduction step, we show that it is possible to design composite controllers that stabilize the (non-hyperbolic) origin.

Keywords:Networked control systems, Model/Controller reduction, Large-scale systems Abstract: In this paper, we propose a new definition of a controllability Gramian for semistable systems, which is then used for model reduction of network systems. The system under consideration is modeled as single integrators that interconnected with each other according to a connected directed network. In the proposed method, the complexity of the network is reduced through a graph clustering method which aggregates the vertices if they respond similarly with respect to external inputs. Here, the similarity of the vertices is computed based on the new Gramian. The reduced-order model is obtained by a Petrov-Galerkin projection where the projection matrices are constructed from the resulting clustering. The reduced system preserves the network structure, and the approximation error between the full-order and reduced-order models is shown to be always bounded. Finally, the proposed approach is illustrated by an example.

Keywords:Network analysis and control, Game theory, Distributed control Abstract: We define and analyze a novel concept of equilibrium over a network, which we refer to as location equilibrium. Its applications include area coverage for taxi drivers, human migration and task assignment for a server network. In particular, we show that a specific instance of the location equilibrium problem is equivalent to the Wardrop equilibrium problem on a specific network. Further, we show that finding a location equilibrium is equivalent to solving a variational inequality with an operator which is in general not monotone. Based on the relation with the Wardrop equilibrium, we propose the use of the extragradient algorithm and show its convergence to a specific location equilibrium. The findings are applied to a numerical study of area coverage for taxi drivers in Hong Kong.

Keywords:Game theory, Network analysis and control, Variational methods Abstract: In this paper, we show that opinion dynamics in social networks as considered in the literature are proximal dynamics in multi-agent network games. We adopt an operator theoretic perspective to show global convergence to an equilibrium for multi-dimensional, interdependent, constrained opinion dynamics on social networks. We illustrate opinion dynamics on complex networks via numerical simulations.

Keywords:Game theory, Variational methods, Agents-based systems Abstract: In this paper, we propose a distributed algorithm for computation of a generalized Nash equilibrium (GNE) in noncooperative games over networks. We consider games in which the feasible decision sets of all players are coupled together by a globally shared affine constraint. Adopting the variational GNE as a refined solution, we reformulate the problem as that of finding the zeros of a sum of monotone operators through a primal-dual analysis and an augmentation of variables. Then we introduce a distributed algorithm based on forward-backward operator splitting methods. Each player only needs to know its local objective function, local feasible set, and a local block of the affine constraint, and share information with its neighbours. We show convergence of the proposed algorithm for fixed step-sizes under some mild assumptions. Numerical simulations are given for networked Cournot competition with bounded market capacities.

Keywords:Optimization algorithms, Large-scale systems, Optimization Abstract: Many problems of interest for cyber-physical network systems can be formulated as Mixed Integer Linear Programs in which the constraints are distributed among the agents. In this paper we propose a distributed algorithm to solve this class of optimization problems in a peer-to-peer network with no coordinator and with limited computation and communication capabilities. In the proposed algorithm, at each communication round, agents solve locally a small LP, generate suitable cutting planes, namely intersection cuts and cost-based cuts, and communicate a fixed number of active constraints, i.e., a candidate optimal basis. We prove that, if the cost is integer, the algorithm converges to the lexicographically minimal optimal solution in a finite number of communication rounds. Finally, through numerical computations, we analyze the algorithm convergence as a function of the network size.

Keywords:Agents-based systems, Optimization, Randomized algorithms Abstract: We consider a multi-agent resource sharing problem that can be represented by a linear program. The amount of resource to be shared is fixed, and each agent adds to the linear cost and constraint a term that depends on some randomly extracted parameters, thus modelling heterogeneity among agents. We study the probability that the arrival of a new agent does not affect the optimal value and the resource share of the other agents, which means that the system cannot accommodate the request of a further agent and has reached its saturation limit. In particular, we determine the maximum number of requests for the shared resource that the system can accommodate in a probabilistic sense. This result is proven by first formulating the dual of the resource sharing linear program, and then showing that this is a random linear program. Using results from the scenario theory for randomized optimization, we bound the probability of constraint violation for the dual optimal solution, and prove that this is equivalent with the primal optimal value and resource share remaining unchanged upon arrival of a new agent. We discuss how this can be thought of as probabilistic sensitivity analysis and offer an interpretation of this setting in an electric vehicle charging control problem.

Keywords:Game theory, Network analysis and control, Agents-based systems Abstract: We analyze the competition between two firms when each firm's market share depends on the average opinion of the consumer population about their respective products. All the consumers interact with each other through a social network and these interactions result in a certain dynamics of their opinion. Each firm attempts to sway the public opinion to its own side by spending money on advertising or other marketing tools (like discounts) on specific consumers. We propose a novel model in which the firms are aware of the opinion dynamics and the structure of the social network. As a result, they can prioritize certain consumer nodes over others based on the social graph. We tackle the problem by defining an appropriate static game model and conduct the corresponding equilibrium analysis. Our results are illustrated by a numerical performance analysis which provides several insights into the choice of investment strategies and how they relate to the consumer social network.

Keywords:Energy systems;Building and facility automation, Predictive control for linear systems Abstract: This paper deals with the problem of minimizing the electricity bill of smart buildings equipped with centralized heating systems and thermal and electrical storage devices. Building participation in a Demand-Response program in the form of price-volume signals is also considered. The proposed solution is based on a Model Predictive Control approach to operate the heating system and the storage devices in an optimal fashion, under thermal comfort and technological constraints.

Keywords:Energy systems, Game theory, Optimization Abstract: This paper analyzes the impact of peer effects on electricity consumption of a network of rational, utility-maximizing users. Users derive utility from consuming electricity as well as consuming less energy than their neighbors. However, a disutility is incurred for consuming more than their neighbors. To maximize the profit of the load-serving entity that provides electricity to such users, we develop a two-stage game-theoretic model, where the entity sets the prices in the first stage. In the second stage, consumers decide on their demand in response to the observed price set in the first stage so as to maximize their utility. To this end, we derive theoretical statements under which such peer effects reduce aggregate user consumption. Further, we obtain expressions for the resulting electricity consumption and profit of the load serving entity for the case of perfect price discrimination and a single price under complete information, and approximations under incomplete information. Simulations suggest that exposing only a selected subset of all users to peer effects maximizes the entity's profit.

Keywords:Energy systems, Modeling, Simulation Abstract: This paper examines the problem of designing a hybrid photovoltaic (PV)/battery system to achieve maximum power point tracking (MPPT) "by design", or, passively, without requiring active control. In contrast to the existing literature on passive PV MPPT, a key goal of the paper is to derive analytic design rules for achieving passive MPPT, as well as dynamic models for the potential departure from MPPT over time. We use a "self-balancing", hybrid PV array and Lithium (Li) ion cell integration topology to demonstrate the idea of "MPPT by design" in this work. A small signal analysis is performed on the system to understand the effects of changing solar irradiation on the state of the system and its self-balancing behavior. In this analysis, we further identify design parameters of the system that enable the hybrid system to achieve MPPT. Finally, a simulation study validates the theoretical propositions of this paper.

Keywords:Energy systems, Optimal control;Automotive control Abstract: The ability of Lithium-ion batteries to perform work decreases at low temperature of operation; a common strategy to improve their productivity is to warm them. In our recent works we have raised the need to study the energy-optimal warm-up of batteries. More recently, our work used battery temperature to determine if the battery has warmed up. The power capability of batteries is a more relatable and arguably useful means to terminate the warm-up process. This work builds upon its predecessors by using power capability as stopping condition, analyzes the problem and numerically solves the same. Subsequently, the relation between the minimum-time and minimum-energy warm-up problem formulations that employ temperature and power constraints to terminate warm-up are theoretically established.

Keywords:Energy systems, Predictive control for nonlinear systems, Delay systems Abstract: In this paper we present a model-based control approach for autonomous flight of kites for wind power generation. Predictive models are considered to compensate for delay in the kite dynamics. We apply Model Predictive Control (MPC), with the objective of guiding the kite to follow a figure-of-eight trajectory, in the outer loop of a two level control cascade. The tracking capabilities of the inner-loop controller depend on the operating conditions and are assessed via a frequency domain robustness analysis. We take the limitations of the inner tracking controller into account by encoding them as optimisation constraints in the outer MPC. The method is validated on a kite system in tow test experiments.

Keywords:Energy systems Abstract: We formulate and analyze a day-ahead (DA) electricity market in which a thermal power plant and a renewable generator compete with each other in their commitments to the market. The market price of energy is affected by the commitments of the generators. The renewable generator faces a settlement cost if it cannot meet its commitment due to unpredictable generation. As a hedge against this cost, it is equipped with a natural gas reserve that can either be used to compensate generation shortages or be sold in the natural gas market. We model the problem as a Stackelberg game, in which an independent system operator (ISO) sends a price signal to the generators. In response, the generators decide on their commitments to maximize their own profit. The ISO decides on the price such that the total commitment will be equal to the energy demanded by the (estimated) load. We develop sufficient conditions for the uniqueness of Nash equilibrium and obtain a quantitative solution for the Nash equilibrium. It is observed that the market price of energy is lower when the renewable generator is equipped with natural gas reserves. Furthermore, when the renewable generator is equipped with natural gas reserves, the commitments of the generators to the market are less affected by the variance of the renewable energy generation. It is also shown that a larger portion of the natural gas reserves are used for electricity generation when the renewable energy generation has higher uncertainty. Thus, the natural gas reserves act as an effective hedge against the variance in the generation of renewable energy.

Keywords:Flight control;Switched systems, Estimation Abstract: This paper presents design of altitude controller with disturbance compensation for an indoor blimp robot and its realization. Due to hardware restrictions, the altitude control behavior of blimp is modeled as a switched system with fixed time delay accompanying with uncertain bounded disturbances. HOMD differentiator is used as an observer for vertical velocity and switching signal estimation. Then a predictor-based controller is conceived, in order to compensate the perturbation, the method for disturbance evaluation is designed. Control scheme is implemented by Matlab Simulink, finally, the performance of improved blimp altitude controller is verified in experiments.

Keywords:Flight control;Aerospace, Optimal control Abstract: The main contribution of this paper is to propose a two-part approach to determine the optimal state-feedback solution for the cruise economy mode problem of a flight management system for a jet powered aircraft. This is currently an open problem for which analytical solutions are only available for suboptimal speeds. The paper assumes that the aircraft is cruising below its drag divergence Mach number at constant altitude. The problem is formulated as the minimization of a direct operating cost parameterized by a coefficient index which is the ratio of the cost of a unit of time to the cost of a unit of weight of fuel burned. The approach is summarized in an algorithm that can be programmed on a flight management system. By using the methods outlined in this paper and summarized in Algorithm 1, the optimal speed, final weight, final cruising time, and total cost of cruise can be approximated with negligible error. A numerical example of an Airbus A320 will illustrate the proposed algorithm.

Keywords:Flight control, Optimal control, Aerospace Abstract: This paper presents a unified control methodology that can be used to formulate, solve, interpret geometrically, and compare the solutions of optimal flight management systems and optimal production planning trade-off problems. The paper focuses on the problem of production of one good at the cost of the depletion of one resource. By production planning it is meant more than just an industrial plant or process. The concept can be extended for example to the management of natural resources when natural forest must be consumed at the expense of agricultural production. The main contribution of this paper is to prove that under strict convexity assumptions such problems have a common and unique feedback solution that depeds on a trade-off parameter called cost index. Furthermore, the solution can be interpreted geometrically using the concept of convex conjugate function and Legendre transformation. The feedback solution yields an analytic expression for the case of zero cost index. When the cost index is positive but small a Taylor series approximation is proposed as a suboptimal analytical solution. It is shown that as the cost index increases the optimal input also increases. This is due to the fact that the tangency point of the supporting line of the rate of depletion that passes through the origin shifts in the positive direction of the input axis. Several examples show the successful application of the theory yielding analytical feedback solutions for flight management systems that can reproduce the maximum range formulas when the cost index is zero.

Keywords:Flight control, Robust control, Uncertain systems Abstract: Circulation Control (CC) is an effective technique to increase lift and improve aerodynamic efficiency of Unmanned Aerial Vehicles (UAVs). This paper introduces a novel, robust nonlinear controller for the longitudinal flight dynamics of a CC-based fixed-wing Unmanned Aerial Vehicle (UCCAV) that operates in cascaded fashion and takes into account changing mass. The controller consists of a dynamic inversion inner-loop and a mu-synthesis outer-loop controller. CC introduces changes of the aerodynamic coefficients that are difficult to determine using strict mathematical formulas. This creates a specific type of model uncertainty in the UCCAV model, which is tackled by the use of mu-analysis. Simulation results demonstrate the efficacy of the proposed control scheme and the ability of the UCCAV to adapt to challenging CC-on-demand scenarios. The technique can be generalized and applied on CC-based and conventional UAVs, seeking to address uncertainty challenges regarding the aircraft's aerodynamic coefficients.

Keywords:Fault tolerant systems, Variable-structure/sliding-mode control;Aerospace Abstract: This paper presents the results of flight tests of a fault tolerant sliding mode controller implemented on the Japan Aerospace Exploration Agency's Multi-Purpose Aviation Laboratory aircraft. These represent the first validation tests of a sliding mode control allocation scheme on a piloted flight test. In this scheme, information about the actuator faults is assumed to be estimated online from a fault detection unit and the available actuators are fully utilized in the presence of actuator faults, in an effort to retain nominal fault free performance. Specifically the flight tests results demonstrate good lateral-directional state tracking performance in the fault free case with no visible performance degradation in the presence of rudder and aileron faults. In fact, during the flight test, the evaluation pilot did not detect any degradation in manoeuvrability when the actuator faults occurred.

Keywords:Flight control, Uncertain systems, Simulation Abstract: At the Institute of Flight System Dynamics of the Technische Universitaet Muenchen (TUM), a digital auto flight system for a fixed-wing Unmanned Air Vehicle (UAV) featuring a novel diamond-shaped configuration is designed, implemented and tested up to its first flight. The capabilities of the UAV comprise a fully automated flight, including ground control for centerline tracking and runway alignment during automatic take-off and landing. This paper presents the approach concerning the development of the model-based designed ground controller for the diamond-shaped UAV. The respective landing gear model required for control law synthesis has been implemented by the Institute of System Dynamics and Control of the German Aerospace Center (DLR) utilizing available configuration and test-bench data. The focus within this paper is set on the dedicated ground controller design based on the associated landing gear model, culminating in promising real live taxi-test results by exciting significantly the ground controller by lateral offset commands.

Keywords:Agents-based systems, Communication networks, Robust control Abstract: This paper studies network coordination over noisy communication channels. The communication between network nodes is corrupted by unknown but bounded noises, which may arise from low-quality channels and/or malicious attacks in the form of data manipulation. We propose a novel coordination scheme, which ensures practical consensus in the noiseless case, while preserving boundedness and coordination properties in the noisy case. The proposed scheme does not require any global information about the network parameters and/or the operating environment (the noise characteristics). Moreover, the communication between nodes occurs only at discrete time instants, and nodes can sample independently and in an aperiodic manner, which renders the scheme suitable for practical implementation in distributed sensor networks.

Keywords:Communication networks, Control of networks, Lyapunov methods Abstract: The devices that constitute the Internet of Things are evolving to include more than just enabling sensing and actuation over a wireless interface. In a contemporary scenario, these devices perform tasks and contribute to an aggregate information flow, in a distributed manner. In the wake of this evolution, new distributed Internet of Things frameworks have emerged. These frameworks maintain a distributed shared state in a distributed hash table. An Internet of Things system's ability to make decisions autonomously and distributively depend on the level of consistency of the shared state. As wireless resources are scarce, the amount of deferred state information in each device is unknown when the system is highly utilised. In this paper, we have developed a controller with the objective to achieve a bounded time-average of deferred state information in each device while maintaining system stability. The controller is derived using Lyapunov drift optimisation with penalty. The resulting controller can successfully bound the shared state consistency level within a narrow margin, maintain system stability, and balance the traffic flow trade-off more successfully than comparable and conventionally used methods.

Keywords:Communication networks;Information theory and control, Optimal control Abstract: Communication energy in a wireless network of mobile autonomous agents should be considered as the sum of transmission energy and propulsion energy used to facilitate the transfer of information. Accordingly, communication-theoretic and Newtonian dynamic models are developed to model the communication and locomotion expenditures of each node. These are subsequently used to formulate a novel nonlinear optimal control problem (OCP) over a network of autonomous nodes. It is then shown that, under certain conditions, the OCP can be transformed into an equivalent convex form. Numerical results for a single link between a node and access point allow for comparison with known solutions before the framework is applied to a multiple-node UAV network, for which previous results are not readily extended. Simulations show that transmission energy can be of the same order of magnitude as propulsion energy allowing for possible savings, whilst also exemplifying how speed adaptations together with power control may increase the network throughput.

Keywords:Communication networks, Network analysis and control, Game theory Abstract: With increasing connectivity among comprising agents or (sub-)systems in complex systems, there is a growing interest in better understanding interdependent security and finding an effective means of dealing with inefficiency in security investments. As a first step toward this goal, we study the problem of approximating the total contributions of agents to social cost, which differ from their private costs, with the help of a population game and the Chung-Lu random graph model.

We first establish an interesting relation between the local minimizers of social cost and the Nash equilibria of a population game with slightly altered costs. Second, under a mild technical assumption, we demonstrate that there exists a unique minimizer of social cost and it coincides with the unique Nash equilibrium of the aforementioned population game. This finding tells us how to modify the private cost functions of selfish agents in order to encourage additional investments in security, in the process improving social efficiency.

Keywords:Communication networks;Quantum information and control, Computer-aided control design Abstract: We consider a network design problem in which one must choose which hubs in a classical communication network to build so as to optimally trade off between the long-term average value derived from the links between hubs and the long-term average cost of maintaining each hub. The optimization is naturally formulated as a 0-1 quadratic programming problem, a non-convex optimization with binary decision variables. This problem is NP-Hard, and has no known polynomial-time approximation scheme (PTAS) for the general case. We explore the performance of quantum annealing on this problem, using a D-Wave quantum processing unit (QPU), and compare performance to exact and simulated annealing solvers running on a modern classical computer. We discuss the relative strengths and weaknesses of these methods. In particular, we focus on the quality of the solutions as well as the computation time associated with each method.

Keywords:Sensor networks, Communication networks, Agents-based systems Abstract: This paper studies the problem of determining sensor locations in a large sensor network using only relative distance (range) measurement. Based on the barycentric coordinate representation, our work generalizes the DILOC algorithm in an asynchronous communication environment with time delays and packet losses. Firstly, an asynchronous communication protocol based on barycentric coordinate is developed, and the distributed localization algorithm is mathematically modeled as a linear difference equation with time-varying delays. Next, by using the extended delay graph theory, we prove that the asynchronous iterative model is globally convergent to the true coordinates if: 1) time interval between any two consecutive update time instants is bounded above and below. 2) communication delays and successive packet losses between sensors are finite. Finally, simulation examples are provided to demonstrate the effectiveness of the theoretical result.

Keywords:Information theory and control, Fault detection, Estimation Abstract: Quickly detecting changes in the statistical behaviour of measurements is important in many applications of control engineering involving fault detection and process monitoring. In this paper, we pose and solve minimax robust Lorden and Bayesian quickest change detection problems for situations where the cost of detection delays compounds exponentially. We show that the detection rules that solve our robust quickest change detection problems are also the rules that solve the standard (non-robust) problems specified by least favourable distributions from uncertainty classes of possible distributions that satisfy a stochastic boundedness condition. In contrast to previous robust quickest change detection results with nonlinear detection delay penalties, our results with exponential delay penalties are exact (i.e., they hold for any false alarm constraint and not only in the asymptotic regime of few false alarms). We illustrate our results through simulations.

Keywords:Information theory and control, Linear systems, Control over communications Abstract: In order to conceal the output information of a given linear SISO system, this paper considers a design problem of an artificial noise called privacy input, which is added to the output. To measure the confidential level of the output, we use the mutual information between the output and the concealed output. We then formulate a problem of designing a privacy input and a feedback gain such that a given linear SISO system is mean square asymptotically stable and satisfies a desired confidential level of the output. The solution is a generalization of our previous study. Numerical experiments demonstrate that the result is useful for concealing the transient output information.

Keywords:Information theory and control, Networked control systems, Filtering Abstract: We consider the situation in which a continuous-time vector Gauss-Markov process is observed through a vector Gaussian channel (sensor) and estimated by the Kalman--Bucy filter. Unlike in standard filtering problems where a sensor model is given a priori, we are concerned with the optimal sensor design by which (i) the mutual information between the source random process and the reproduction (estimation) process is minimized, and (ii) the minimum mean-square estimation error meets a given distortion constraint. We show that such a sensor design problem is tractable by semidefinite programming. The connection to zero-delay source-coding is also discussed.

Keywords:Information theory and control, Networked control systems, Linear systems Abstract: In a previous work, we extended the notion of invariance entropy, also known as topological feedback entropy, of deterministic nonlinear control systems to systems with nondeterministic disturbances and showed that this notion of invariance feedback entropy characterizes the necessary data rate of any coder-controller scheme that communicates via a digital, noiseless channel and achieves invariance of a given subset of the state space. In this paper, we derive an intrinsic lower bound of the invariance feedback entropy for linear systems with bounded disturbances in terms of the absolute value of the determinant of the system matrix and a ratio involving the volume of the invariant set as well as the volume of the disturbance set. Additionally, we derive a lower bound of the data rate associated with static, memoryless coder-controllers. If the data rate of a static coder-controller matches the lower bound, we obtain the remarkable property that its data rate is not larger than log_2(1+2^{-h_{rm inv}}) compared to the best dynamically achievable data rate h_{rm inv}. The lower bounds are tight for some classes of systems.

Keywords:Information theory and control, Stochastic optimal control;Building and facility automation Abstract: We study the problem of jointly designing the sensor and controller for a dynamical system driven by a privacy-sensitive input process. This problem is motivated by the modern thermostat control example where home's occupancy is continually monitored and leveraged to tailor thermostat behaviors for better energy savings and comfort, which, however, arouses users' concern over privacy. We start by quantifying the instantaneous privacy loss in a control system under standard inference attacks. We present the closed form of privacy loss for linear Gaussian systems and propose a sampling-based method to approximate privacy loss for general dynamical systems. The optimal control and sensor query strategy for a private-input-driven system is then characterized, and we further prove the validity of separation principle for a linear system with Gaussian disturbance and quadratic cost under the privacy loss proposed in this paper. We close the paper by demonstrating the flexibility of the joint sensor-controller policy in the occupancy-based thermostat control example and providing some insights on the tradeoff among energy, comfort, and privacy.

Keywords:Linear systems, Optimization;Information theory and control Abstract: It is of practical significance to define the notion of a measure of quality of a control system, i.e., a quantitative extension of the classical notion of controllability. In this article we demonstrate that the three standard measures of quality involving the trace, minimum eigenvalue, and the determinant of the controllability grammian achieve their optimum values when the columns of the controllability matrix from a tight frame. Motivated by this, and in view of some recent developments in frame theoretic signal processing, we provide a measure of quality for LTI systems based on a measure of tightness of the columns of the reachability matrix .

Keywords:Identification, Autonomous systems, Estimation Abstract: Airborne Wind Energy (AWE) refers to systems capable of harvesting energy from wind by flying crosswind patterns with a tethered aircraft. Accurate models are crucial for tuning and validation of flight controllers. Due to the non-conventional structure of the airborne component, an intensive flight test campaign must be set where maneuvers are performed for parameter estimation purposes. In this paper, we optimize maneuvers for the longitudinal dynamics of a rigid wing AWE pumping system by solving a model-based experimental design problem that aims to obtain more accurate parameter estimates and reduce the flight test time. We consider a trim reference condition of the aircraft and constraints are enforced in order to prevent flight envelope violation. Finally, the optimal solution is implemented in the Flight Control Computer (FCC) of the prototype developed by Ampyx Power B.V. and validated under realistic flight conditions.

Keywords:Identification, Estimation, Energy systems Abstract: Battery short-term electrical impedance behaviour varies between linear, linear time-varying or nonlinear at different operating conditions. Data based electrical impedance modelling techniques often model the battery as a linear time-invariant system at all operating conditions. In addition, these techniques require extensive and time-consuming experimentation. Often due to sensor failures during experiments, constraints in data acquisition hardware, varying operating conditions and the slow dynamics of the battery, it is not always possible to acquire data in a single experiment. Hence multiple experiments must be performed. In this paper, a local polynomial approach is proposed to estimate nonparametrically the best linear approximation of the electrical impedance affected by varying levels of nonlinear distortion, from a series of input current and output voltage data sub-records of arbitrary length.

Keywords:Network analysis and control, Estimation, Linear systems Abstract: This work examines metrics for target reachability and source observability in dynamical networks, which are especially relevant in a network security context. Specifically, the energy required to control a target node in a network from a remote input is characterized, and dually the fidelity with which a source state can be estimated from a remote measurement is studied. The work highlights an essential asymmetry between the problems: We show that target reachability is often easy, while source observability is almost always impossible. Several spectral and graph-theoretic results are also presented, which give structural insight into how easy or hard target control and source estimation are.

Keywords:Stochastic systems, Nonlinear systems identification, Optimization Abstract: The rational covariance extension problem is a moment problem with several important applications in systems and control as, for example, in identification, estimation, and signal analysis. Here we consider the multidimensional counterpart and present new results for the well-posedness of the problem. We apply the theory to texture generation by modeling the texture as the output of a Wiener system. The static nonlinearity in the Wiener system is assumed to be a thresholding function and we identify both the linear dynamical system and the thresholding parameter.

Keywords:Identification, Estimation, Optimization Abstract: Factor analysis aims to describe high dimensional random vectors by means of a small number of unknown common factors. In mathematical terms, it is required to decompose the covariance matrix Sigma of the random vector as the sum of a diagonal matrix D - accounting for the idiosyncratic noise in the data - and a low rank matrix R - accounting for the variance of the common factors - in such a way that the rank of R is as small as possible so that the number of common factors is minimal. In practice, however, the matrix Sigma is unknown and must be replaced by its estimate, i.e. the sample covariance, which comes from a finite amount of data. This paper provides a strategy to account for the uncertainty in the estimation of Sigma in the factor analysis problem.

Keywords:Identification, Estimation Abstract: Adding input and output noises for increasing model identification error of finite impulse response (FIR) systems is considered. This is motivated by the desire to protect the model of the system as a trade secret by rendering model identification techniques ineffective. Optimal filters for constructing additive noises that maximizes the identification error subject to maintaining the closed-loop performance degradation below a limit are constructed. Furthermore, differential privacy is used for designing output noises that preserve the privacy of the model.

Keywords:Cooperative control, Constrained control, Stability of nonlinear systems Abstract: This paper investigates the robust semi-global containment control problem for a group of linear systems with imperfect actuators. The imperfect actuators are characterized by nonlinearities such as saturation and dead zone and their input output relationships are not precisely known. The dynamics of follower agents are also affected by the input additive disturbances. Low-and-high gain feedback consensus protocols are constructed. It is shown that robust semi-global containment control can be achieved when each follower agent has access to the information of at least one leader agent. Numerical simulation illustrates the theoretical results.

Keywords:Cooperative control;Hybrid systems, Optimization Abstract: We address the issue of identifying conditions under which the centralized solution to the optimal multi-agent persistent monitoring problem can be recovered in a decentralized event-driven manner. In this problem, multiple agents interact with a finite number of targets and the objective is to control their movement in order to minimize an uncertainty metric associated with the targets. In a one-dimensional setting, it has been shown that the optimal solution can be reduced to a simpler parametric optimization problem and that the behavior of agents under optimal control is described by a hybrid system. This hybrid system can be analyzed using Infinitesimal Perturbation Analysis (IPA) to obtain a complete on-line solution through an event-driven centralized gradient-based algorithm. We show that the IPA gradient can be recovered in a distributed manner in which each agent optimizes its trajectory based on local information, except for one event requiring communication from a non-neighbor agent. Simulation examples are included to illustrate the effectiveness of this "almost decentralized" algorithm and its fully decentralized counterpart where the aforementioned non-local event is ignored.

Keywords:Cooperative control;Maritime control, Output regulation Abstract: This paper considers formation control in dynamic positioning (DP) of multiple offshore vessels. Toward the higher accuracy, reliability, and robustness demands in the advanced marine vessels, we shall attempt a cooperative robust output regulation based synthesis for the problem. It is shown that, when the vessels modeling parameters in question are unknown, the global robust asymptotic formation control can be systematically approached in a distributed control fashion. Specifically, the controller can be constructed by incorporating suitable internal models and performing a recursive block backstepping design for the augmented system. Hence, it assures accurate formation control with robustness against model uncertainties.

Keywords:Cooperative control, Network analysis and control, Distributed control Abstract: This paper studies a recently proposed continuoustime distributed self-appraisal model with time-varying interactions among a network of n individuals which are characterized by a sequence of time-varying relative interaction matrices. The model describes the evolution of the social-confidence levels of the individuals via a reflected appraisal mechanism in real time. We show that when the relative interaction matrices are doubly stochastic, the n individuals’ self-confidence levels will all converge to 1/n, which indicates a democratic state, exponentially fast under appropriate assumptions, and provide an explicit expression for the convergence rate. Numerical examples are provided to verify the theoretical results and to show that when the relative interaction matrices are stochastic (not doubly stochastic), the social-confidence levels of the individuals may not converge to a steady state.

Keywords:Cooperative control, Sensor networks, Optimization Abstract: We consider the optimal coverage problem where a multi-agent network is deployed in an environment with obstacles to maximize a joint event detection probability. We first show that the objective function is monotone submodular, a class of functions for which a simple greedy algorithm is known to be within 1-1/e of the optimal solution. We then derive two tighter lower bounds by exploiting the curvature information of the objective function. We further show that the tightness of these lower bounds is complementary with respect to the sensing capabilities of the agents. Simulation results show that this approach leads to significantly better performance relative to previously used algorithms.

Keywords:Cooperative control, Networked control systems Abstract: The paper presents a finite-time distributed control method for consensus of networked multiple systems, which is different from the traditional methods based on signum function or fractional power state feedback (where the finite convergence time is contingent on initial conditions and the control action is discontinuous or non-smooth). More specifically, the proposed method is built upon the regular state feedback, incorporated with a finite-time scaling function, leading to distributed smooth control action. Furthermore, with this method, the consensus is achieved within prescribed time under bidirectional interaction. Namely, all the agents reach the average consensus in designer-assigned finite time under undirected connected topology. Numerical simulations demonstrate and validate the superiority of the proposed control.

Keywords:Healthcare and medical systems;Hybrid systems, Predictive control for linear systems Abstract: The problem of drug administration in therapeutic treatments of the human immunodeficiency virus is studied by means of impulsive zone model predictive control (iZMPC). Two strategies are presented. The first one is a zone MPC based on a linear impulsive characterization of the system and provides an efficient and easy-to-apply way to find the proper therapeutic treatments. The second one, which is devoted to overcome the often large plant-model mismatches, is based on a novel nonlinear impulsive prediction scheme. Both proposals are tested and the results appear to be satisfactory as long as the medical goals of the treatment are achieved, which also shows that these impulsive schemes have a great potential in the developing of therapeutic strategies.

Keywords:Variable-structure/sliding-mode control, Biological systems, Modeling Abstract: In the study of the Human Immunodeficiency Virus (HIV) infection dynamics, the reproductive ratio is a well known tool which provides a steady-state condition to determine the outcome of the infection. This paper assesses the control of HIV by the immune response. Dynamical conditions for the containment of HIV infection by the HIV-specific CD8+ T cell response are evaluated using a model of HIV dynamics in vivo in which HIV-infected cells are killed before they start producing new virion. The reachability paradigm from Variable Structure Control (VSC) theory is used to formulate a dynamical condition for immunity. Simulation results show that this reachability condition effectively monitors the immunological requirements to contain HIV. This work also suggests that the cytolytic killing mechanism of CD8+ T cells operates as a boundary layer control to contain HIV infection. Together, the findings indicate that in contrast to the reproductive ratio, the proposed VSC approach delivers a framework to assess the effects of nonlinear dynamics and uncertainty as well as providing a means to investigate immunotherapy strategies.

Keywords:Biomedical, Identification, Biological systems Abstract: Time series measurements of circular viral episome (2-LTR) concentrations enable indirect quantification of persistent low-level Human Immunodeficiency Virus (HIV) replication in patients on Integrase-Inhibitor intensified Com- bined Antiretroviral Therapy (cART). In order to determine the magnitude of these low level infection events, blood has to be drawn from a patients at a frequency and volume that is strictly regulated by the Institutional Review Board (IRB). Once the blood is drawn, the 2-LTR concentration is determined by quantifying the amount of HIV DNA present in the sample via a PCR (Polymerase Chain Reaction) assay. Real time quantitative Polymerase Chain Reaction (qPCR) is a widely used method of performing PCR; however, a newer droplet digital Polymerase Chain Reaction (ddPCR) method has been shown to provide more accurate quantification of DNA. Using a validated model of HIV viral replication, this paper demonstrates the importance of considering DNA quantification assay type when optimizing experiment design conditions. Experiments are optimized using a Genetic Algorithm (GA) to locate a family of suboptimal sample schedules which yield the highest fitness. Fitness is defined as the expected information gained in the experiment, measured by the Kullback-Leibler Divergence (KLD) between the prior and posterior distributions of the model parameters. We compare the information content of the optimized schedules to uniform schedules as well as two clinical schedules implemented by researchers at UCSF and the University of Melbourne. This work shows that there is a significantly greater gain information in experiments using a ddPCR assay vs. a qPCR assay and that certain experiment design considerations should be taken when using either assay.

Keywords:Biological systems;Biomedical, Systems biology Abstract: HIV/AIDS drug therapies, including highly active anti-retroviral therapy (HAART), often fail because of the emergence of drug resistant mutation. We research a new evaluation method for the possibility of the resistant emergence in this paper. We suggest a quantitative estimation method to evaluate this possibility for antiretroviral drug treatment of HIV infection. By doing so, this paper reports research on quantitative description of the emergence of drug resistant HIV for drug therapies. By means of simulation studies we compare HIV/AIDS therapies by applying the evaluation method to the therapies.

Keywords:Healthcare and medical systems, Compartmental and Positive systems;Biomedical Abstract: The mathematical properties as positivity and positive invariance are helpful for Type 1 Diabetes mellitus (T1DM) system model to formalize the constraints of positivity on glycemia and insulinemia and the positivity of insulin infusion. Nevertheless, they are seldom used in this context. In this paper, the largest Positively Invariant Set (PIS) is computed for the open-loop system (where only a basal insulin rate is infused). Its practical interest is that hypoglycemia is prevented for any trajectory initiated inside the PIS and hypoglycemia is predicted outside the PIS. These results can be used in both open-loop usual basal-bolus therapy or to support any closed-loop control algorithm to avoid hypoglycemia.

Keywords:Agents-based systems, Network analysis and control, Cooperative control Abstract: This paper studies the effect of human awareness on a distributed continuous-time bi-virus model in which two competing viruses diffuse over a network comprised of multiple groups of individuals. When contacting infected individuals in their own and neighboring groups, individuals may either be infected by one of the two viruses with a virus-dependent infection rate or become alert. Alert individuals may be infected by either virus but with a smaller virus-dependent infection rate, and the alert state also diffuses over the network. Limiting behaviors of the model are studied by analyzing the equilibria of the system and their stability. Both equilibria and their stability are compared with those of the model without human awareness.

Keywords: Abstract: This paper is dedicated to Dr. Roberto Temple. The topic, outliers in system identification, was initially started with a discussion with Roberto in the early 2000. The first part of the paper summaries the approach, optimization with few violated constrains for linear bounded error parameter estimation, that was appeared in IEEE Trans. on Automatic Control. The topic was revisited by a new approach, system identification in the presence of outliers and random noise by compressed sensing, is summarized in the second part of the paper which was appeared in Automatica.

Keywords:Networked control systems, Communication networks, Distributed control Abstract: Networked systems often relies on distributed algorithms to achieve a global computation/statistic goal with iterative local information exchanges between neighbor nodes. Due to the concerns of privacy, one node usually adds a random noise to its original data for information exchange at each iteration to preserve the privacy. But a neighbor node can still infer/estimate the nodeâ€™s state based on the information it received, no matter what type of noises is used. However, how to obtain the optimal state estimation is a critical and open issue. Therefore, in this paper, we investigate how to obtain the optimal estimation of each nodeâ€™s state based on the information outputs of the node and its neighbors. Firstly, we introduce two novel definitions on the estimation, named ?- accurate estimation and optimal state estimation, to depict the optimal state estimation problem. Then, we obtain the optimal state estimation and its closed-form of expression, followed with some important properties considering different available information sets for the estimation.

Keywords:Networked control systems;Control applications, Distributed control Abstract: This paper addresses the problem of a distributed lighting strategy for the arrangement of a lighting system consisting of a group of light emitting diode (LED) lamps and workspace zones. In a commercial building, the deployment of a large number of lamps in a distributed manner poses a great challenge in achieving energy efficiency and personalized lighting. In this paper, we present a distributed control approach to determine the dimming levels of LED sources by using local occupancy information. The advantages of this approach are that i) each LED light only exchanges control information with neighbors adjacent to it based on a communication module; ii) users' comfort is managed by limits on the rate of the illuminance change of each light; iii) the calculation of reference dimming level at each light is decreased. Then, a specific distributed optimization problem is formulated with constraints on the bounded illuminance change and the control information exchange. Subsequently, a distributed receding horizon algorithm is developed for the closed-loop control. To this end, the convergent property of the proposed distributed approach is also proved. Simulation studies are given to verify the validity of the proposed approach.

Keywords:Networked control systems, Control of networks, Optimal control Abstract: This paper considers the scheduling problem of a decentralized system where a number of dynamical subsystems with no computational power are scheduled to transmit their measurements via a resource-limited communication network to a remote decision maker who acts as an estimator, controller and scheduler for the subsystems. We propose a new approach for communication resource allocation for a wide class of objective functions, for both coupled and decoupled systems, and for both scheduling observations as well as control commands. This framework facilitates scheduling over a finite horizon and can explicitly deal with stochastic channels. For decoupled subsystems, we propose the notion of cost of information loss (CoIL) and we demonstrate that the communications resource allocation problem can be directly expressed in terms of CoIL functions as an assignment-type optimization problem. Illustrative examples demonstrate how communication resources affect the performance of the system.

Keywords:Networked control systems, Control of networks, Stability of nonlinear systems Abstract: In the consensus problem considered in this paper, each agent can impose a lower and an upper bound on the achievable consensus values. We show that if such state constraints are implemented by saturating the value transmitted to the neighboring nodes, the resulting constrained consensus problem must converge to the intersection of the intervals imposed by the individual agents.

Keywords:Networked control systems, Control over communications, Autonomous systems Abstract: We consider a mobile robot tasked with gathering data in an environment of interest and transmitting these data to a data center. The task is specified as a high-level Linear Temporal Logic (LTL) formula that captures the data to be gathered at various regions in the workspace. The robot has a limited buffer to store the data, which needs to be transmitted to the data center before the buffer overflows. Communication between the robot and the data center is through a dedicated wireless network to which the robot can upload data with rates that are uncertain and unknown. In this case, most existing methods based on dynamic programming can not be applied due to the lack of an accurate model. To address this challenge, we propose here an actor-critic reinforcement learning algorithm where the task execution, workspace exploration, and parameterized-policy learning are all performed online and simultaneously. The derived motion and communication control strategy satisfies the buffer constraints and is reactive to the uncertainty in the wireless transmission rate. The overall complexity and performance of our method is compared in simulation to static solutions that search for constrained shortest paths, and to existing learning algorithms that rely on the construction of the product automaton.

Keywords:Distributed control, Networked control systems, Network analysis and control Abstract: In this paper, the novel consensus protocol on the unit circle is proposed. The consensus problem on the unit circle refers to the synchronization of coupled oscillators. We consider the states of the agents on the unit circle as the position vector in the vector space. We add an auxiliary variable which is assumed to be communicated by agents. We design the control law defined in the vector space for the synchronization by using the auxiliary variables. From the convexity of the vector space, we guarantee the convergence of the auxiliary variables under the proposed consensus protocol for almost all initial conditions. With the proposed control input, the dynamics of agents on the circle achieves synchronization exponentially. By the projection of the proposed algorithm onto the circle, it is not necessary to analyze the dynamics of the agent directly on the circle. Instead, the global convergence property of the agent is analyzed in the vector space.

Keywords:Behavioural systems, Optimal control, Linear systems Abstract: In this paper, we bring out a link between storage functions of allpass systems and observability/controllability Gramians. We show that under a particular transformation, the storage function of an allpass system is induced by an identity matrix. Interestingly, certain algebraic relations between the states and costates/dual states of an allpass system capture the information of the storage function of the system. Further, we also prove that certain difference dynamics between states and costates of an allpass system is always present in the orthogonal complement of its controllable subspace.

Keywords:Behavioural systems, Stability of linear systems, Linear systems Abstract: In this paper, we have characterized a set of linear controllers to guarantee a stable interconnection with a given plant. We consider the plant to be dissipative with respect to a general power supply, which satisfies a spectral factorizability condition. We show in this paper that the interconnection of the plant with a controller is guaranteed to be stable if we choose a controller that is also dissipative with respect to a new supply rate. This new supply rate is determined by the supply rate of the plant and the interconnection topology. We further show how this result can be applied to special cases of Nyquist-plot-compatible supply rates and supply rates that are obtained by mixing two different supply rates. An upshot of the material presented in this paper is that stability assurance due to passivity, small-gain, negative imaginary, and their mixtures are all special cases of stability due to dissipativity.

Keywords:Linear systems, Large-scale systems Abstract: We ask for simple controllers to stabilize linear systems. Thereby, we focus on what we call separable controllers, i.e., controllers which can be factorized into a Kronecker product of two smaller matrices. It is shown that a certain class of linear systems can be stabilized by such controllers. Consequently, the complexity of separable controllers, quantified in terms of the number of variables required to represent the controller, say on a computer, is minimized. The latter yields number theoretic insights into our problem.

Keywords:Linear systems, Control of networks, Optimization Abstract: In this paper, we address the constrained design of continuous-time linear dynamics to improve system control performance, which can be measured as a function of the controllability Gramian. In contrast with the problem of deployment of actuation capabilities to achieve a specified control performance, we seek to change the dynamics of linear systems while considering the deployed actuation mechanisms. Specifically, we consider spectral properties of the `infinite' controllability Gramian as control performance metrics, and apply constrained (i.e., bounded) perturbations in the system's parameters while respecting its structure. We show that two different (yet related) re-design problems for control enhancement can be cast as bilinear or linear matrix equality problems. Lastly, we propose different strategies to obtain the solution of these problems, and assess their performance in the context of multi-agent networks in the leader-follower setup.

Keywords:Linear systems, Distributed control, Optimization Abstract: We consider the optimal control of linear time-invariant (LTI) systems via self-triggered sparse optimal control (SSOC) laws. Our control objective is to design an optimal control law which stabilizes the LTI system for all initial conditions, requires less sensing, minimizes communication requirements between the subsystems, minimizes the number of active actuators, and provides guaranteed closed-loop performance bounds. To achieve such control objectives, we formulate a sequence of ell_0-regularized linear-quadratic optimal control problems, wherein the objective is to optimize a cost function which involves three penalizing terms: one for maximizing the inter-execution time, another one for minimizing the number of nonzero elements of the state feedback gain, and the last one for minimizing the number of active actuators. Deriving a lower bound on inter-execution times, we propose a scheme to solve this problem. First, the ell_1-relaxation is utilized to cast the problem as a semi-definite program (SDP) to compute a feedback gain while the inter-execution time is kept fixed. Second, a nonlinear equation is solved to determine the inter-execution time while the feedback gain is kept fixed. The proposed SSOC law is feasible and results in a stabilizing sequence of sparse optimal controllers. Additionally, the performance of the resulting closed-loop system does not exceed a prespecified performance bound. By numerical simulations, sparsity in time/space is improved compared to periodic time-triggered LQR design. Moreover, a tradeoff between prespecified performance bound and sparsity in time/space is observed. Finally, the paper is concluded by drawing some future directions.

Keywords:Linear systems, Large-scale systems, Distributed control Abstract: In this paper, a notion of non-fragility is introduced for a state feedback controller which stabilizes a linear time-invariant (LTI) system. Then, lower and upper bounds on such a non-fragility are derived. Based on such derived bounds on non-fragility, a sparsification procedure is proposed to get sparsified state feedback controllers. Investigating the various numerical experiments, it is observed the proposed method is applicable to large-scale systems consisting of thousands of states. Additionally, it is shown in some case studies, the (non-fragilty)-based sparsification procedure outperforms a well-respected existing method in terms of sparsity-performance tradeoff behavior. Also, considering a set of sparse stabilizing state feedback controllers, a tradeoff between upper bound on non-fragility and sparsity level of such state feedback controllers is visualized.

Keywords:Discrete event systems;Automata;Delay systems Abstract: We address, in this paper, the problem of codiagnosability of timed networked discrete event systems (TNDES) subject to delays and losses of observations of events between the measurement sites (MS) and local diagnosers (LD), and, for this purpose, we first introduce a new timed model that represents the dynamic system behavior of the plant based on the, a priori, knowledge of the minimal firing time for each transition of the plant and on the maximal delays in the communication channels that connect MS and LD, and also intermittent packet losses in the communication network, we then convert this timed model in an untimed one, and, based on the untimed model, we present necessary and sufficient conditions for TNDES codiagnosability and an automaton-based algorithm for its verification. Examples illustrate all the results present in the paper.

Keywords:Discrete event systems;Automata;Supervisory control Abstract: We investigate the enforcement of opacity, an information-flow privacy property, using insertion decisions that modify the output of the system by event insertions. Previous work considered the problem of enforcing opacity under the assumption that the insertion functions were based on the observed system strings. Now, we investigate the more powerful method of insertion decisions based on the exact system states and events. In this case, the insertion function would be embedded into the system itself, rather than being an output interface. In this paper we develop computationally efficient methods that (i) verify if a valid insertion function exists in this setting; and (ii) if one exists, synthesize one using a computationally effective algorithm.

Keywords:Discrete event systems;Automata;Supervisory control Abstract: We study the security of Cyber-Physical Systems (CPS) in the context of the supervisory control layer. Specifically, we propose a general model of a CPS attacker in the framework of Discrete Event Systems (DES) and investigate the problem of synthesizing an attack strategy for a given controlled system. Our model captures a class of textit{deception attacks}, where the attacker has the ability to modify a subset of sensor readings and mislead the supervisor, with the goal of inducing the system into an undesirable state. We introduce a new type of a bipartite transition structure, called textit{Insertion-Deletion Attack structure} (IDA), to capture the game-like interaction between the supervisor and the environment (which includes the system and attacker). This structure is a discrete transition system that embeds information about all possible attacker's stealthy actions, and all states (some possibly unsafe) that become reachable as a result of those actions. We present a procedure for the construction of the IDA and discuss its properties. Based on the IDA, we discuss the characterization of successful stealthy attacks, i.e., attacks that avoid detection from the supervisor and cause damage to the system.

Keywords:Discrete event systems, Intelligent systems Abstract: Unsupervised learning of concepts is essential for analogical thinking, which is considered a hallmark of human intelligence. We argue that signals in the brain constantly generate relations among their constituents. Unsupervised learning is then accomplished by fast similarity testing of the stored relation sets using resonance transients and time sequenced memory units. We discuss specific algorithms to achieve these functions.

Keywords:Discrete event systems, Lyapunov methods, Stability of nonlinear systems Abstract: In this paper we study a dynamic property of a class of discrete-time systems. Such a property, that we call discrete-homogeneity, is verifiable algebraically in the transition map of the system. Discrete-homogeneity allows to establish stability features of the system by considering only the discrete-homogeneity degree. Such stability properties are studied by means of Lyapunov and Lyapunov-like functions.

Keywords:Discrete event systems;Petri nets;Control applications Abstract: Max-plus linear systems are often used to model timed discrete-event systems, which represent system operations as discrete sequences of events in time. This paper presents the observer-based controller to solve the disturbance decoupling problem for max-plus linear systems where only estimations of system states are available for the controller. This observer-based controller leads to a greater control input than the one obtained with the output feedback strategy based on just-in-time criterion. A high throughput screening system in drug discovery illustrates this main result by showing that the scheduling obtained from the observer-based controller solving the disturbance decoupling problem is better than the scheduling obtained from the output feedback controller.

Keywords:Iterative learning control, Lyapunov methods, Robust adaptive control Abstract: In this paper, we investigate the robust adaptive iterative learning control issue for discrete-time nonlinear systems with uncertainties by using an iteration-varying dead-zone approach. The uncertainties are dependent on both time index and iteration index, which can be expressed as the sum of a time-varying but iteration-invariant part and a bounded iteration-drift term. Then, a novel iteration-varying dead-zone approach is employed in the estimations of the iteration-invariant part and the drift bound simultaneously. The ultimate error bound is given explicitly with rigorous proof. Finally, simulation result has illustrated the efficacy of the proposed approach.

Keywords:Lyapunov methods;Aerospace, Stability of nonlinear systems Abstract: We model a point mass load tethered to two aerial vehicles, and propose a control strategy that guarantees that the load tracks a desired position trajectory. Our framework consists in designing an input and a state transformation which converts the quadrotors-load system into three decoupled subsystems: one concerning the position of the load, with dynamics similar to those of an under-actuated aerial vehicle; one concerning the angle between the cables, with double integrator dynamics; and another concerning the yaw motion of the plane formed by the cables, also with double integrator dynamics. Once the decoupling is done, controllers from the literature can be leveraged, which we take advantage of when controlling the subsystem with dynamics similar to those of an under-actuated aerial vehicle. Simulations are presented which validate the proposed algorithm.

Keywords:Lyapunov methods, Constrained control;Flight control Abstract: We consider a system composed of a bar tethered to two aerial vehicles, and develop a controller for pose tracking of the bar, i.e., a controller for position and attitude tracking. Our first control step is to provide an input and a state transformations which convert the system vector field into one that highlights the cascaded structure of the problem. We then design a controller for the transformed system by exploring that cascaded structure. There are three main contributions: emph{i)} we provide bounds on the linear and angular acceleration of the bar that guarantee well-posedness of the controller, and such bounds can be used when selecting the gains and saturations of bounded controllers for both three dimensional and unit vector double integrators; emph{ii)} the proposed control law includes a degree of freedom which can be used to regulate the relative position between the aerial vehicles; and emph{iii)} the proposed control law for the throttle guarantees that the cascaded structure of the problem is preserved. Simulations are presented which validate the proposed algorithm.

Keywords:Lyapunov methods;Control applications;Power electronics Abstract: In this paper, Lyapunov stability of systems with rational polynomial dynamics is investigated by using sum of squares (SOS) programming methods and polynomial Lyapunov functions. An optimization based algorithm is proposed in order to design a stabilizing controller which maximizes the region of attraction. As the decision variables are being multiplied together, and therefore, the optimization problem is bilinear, the iteration method based on bisection is used in order to gradually enlarge the region of attraction. The positive definiteness conditions are relaxed into SOS conditions. The proposed controller design and stability analysis algorithm is evaluated by simulating a bidirectional power converter feeding a constant power load (CPL).

Keywords:Lyapunov methods, Stability of nonlinear systems, Algebraic/geometric methods Abstract: The paper discusses stabilization of nonlinear discrete-time dynamics in feedforward form. First it is shown how to define a Lyapunov function for the uncontrolled dynamics via the construction of a suitable cross-term. Then, stabilization is achieved in terms of u-average passivity. Several constructive cases are analyzed.

Keywords:Vision-based control, Lyapunov methods Abstract: This paper presents a stable correspondence-free image-based visual servoing approach. Unlike classical image-based visual controllers, explicit feature correspondence (matching) to some desired set is not required before a control input is obtained. Instead, a stabilising control approach is used to simultaneously solve the feature correspondence and visual servoing problem, removing any feature tracking requirement or additional image processing. Stability of the proposed approach is demonstrated via Lyapunov analysis and preliminary results using a free-flying camera are presented.

Keywords:Stochastic optimal control;Hybrid systems, Stochastic systems Abstract: We examine Lagrangian techniques for computing underapproximations of finite-time horizon, stochastic reach-avoid level sets for discrete-time, nonlinear systems. We use the concept of reachability of a target tube to define robust reach-avoid sets which are parameterized by the target set, safe set, and the set which the disturbance is drawn from. We unify two existing Lagrangian approaches to compute these sets, and establish that there exists an optimal control policy for the robust reach-avoid sets which is a Markov policy. Based on these results, we characterize the subset of the disturbance space whose corresponding robust reach-avoid set for a given target and safe set is a guaranteed underapproximation of the stochastic reach-avoid level set of interest. The proposed approach dramatically improves the computational efficiency for obtaining an underapproximation of stochastic reach-avoid level sets when compared to the traditional approaches based on gridding. Our method, while conservative, does not rely on a grid, implying scalability as permitted by constraints due to computational geometry. We demonstrate the method on two examples: a simple two-dimensional integrator, and a space vehicle rendezvous-docking problem.

Keywords:Stochastic optimal control, Optimization, Game theory Abstract: The theory of (standard) stochastic optimal control is based on the assumption that the probability distribution of uncertain variables is fully known. In practice, however, obtaining an accurate distribution is often challenging. To resolve this issue, we study a distributionally robust stochastic control problem that minimizes a cost function of interest given that the distribution of uncertain variables is not known but lies in a so-called ambiguity set. We first investigate a dynamic programming approach and identify conditions for the existence and optimality of non-randomized Markov policies. We then propose a duality-based reformulation method for an associated Bellman equation in cases with conic confidence sets. This reformulation alleviates the computational issues inherent in the infinite-dimensional minimax optimization problem in the Bellman equation without sacrificing optimality. The effectiveness of the proposed method is demonstrated through an application to a stochastic inventory control problem.

Keywords:Stochastic optimal control, Optimization, Markov processes Abstract: We present a scalable underapproximation of the terminal hitting time stochastic reach-avoid probability at a given initial condition, for verification of high-dimensional stochastic LTI systems. While several approximation techniques have been proposed to alleviate the curse of dimensionality associated with dynamic programming, these techniques cannot handle larger, more realistic systems. We present a scalable method that uses Fourier transforms to compute an underapproximation of the reach-avoid probability for systems with disturbances with arbitrary probability densities. We characterize sufficient conditions for Borel-measurability of the value function. We exploit fixed control sequences parameterized by the initial condition (an open-loop control policy) to generate the underapproximation. For Gaussian disturbances, the underapproximation can be obtained using existing efficient algorithms by solving a convex optimization problem. Our approach produces non-trivial lower bounds and is demonstrated on a 40D chain of integrators.

Keywords:Stochastic optimal control, Markov processes Abstract: In this work we construct a basic theory of risk-aware continuous-time Markov decision processes, and even more broadly, that of semi-Markov decision processes. Methods that account for the preferences of risk-aware agents have been introduced and studied in the context of discrete time problems, however, there has been virtually no such development for continuous-time models. We extend the literature of risk-aware optimization to semi-Markov control problems, and consider generic measures of risk with infinite-horizon discounted costs. We show that the optimization problem can be recast into a linear program using occupation measures, and can thus be solved using convex analytic methods. Our results extend the theory of risk-aware (discrete-time) Markov decision problems to the continuous-time setting, and allow optimization of e.g. risk-sensitive queuing systems.

Keywords:Stochastic optimal control, Subspace methods Abstract: The paper studies the stochastic optimal control problem for systems with unknown dynamics. First, and open loop deterministic trajectory optimization problem is solved without knowing the explicit form of the dynamical system. Next, a Linear Quadratic Gaussian (LQG) controller is designed for the nominal trajectory-dependent linearized system, such that under a small noise assumption, the actual states remain close to the optimal trajectory. The trajectory-dependent linearized system is identified using input-output experimental data consisting of the impulse responses of the nominal system. A computational example is given to illustrate the performance of the proposed approach.

Keywords:Stochastic optimal control;Switched systems, Adaptive control Abstract: This paper proposes a model-based approach for the optimal quadratic control of discrete-time Markov jump linear systems (MJLS), in a scenario where the transition probabilities of the Markov chain are uncertain, yet the controller has perfect information of the jump process at all times. We derive an adaptive control strategy that, based on online measurements of the Markov chain, incrementally builds a transition model via maximum-likelihood estimation (MLE) and, at certain time steps, uses it to adjust the current policy in a certainty equivalence fashion. The approach is able to make use of prior information regarding the transition probabilities or, in case this is not available, to address the fully unknown case. Some numerical examples, regarding Samuelson's macroeconomic model and the control of a faulty robotic manipulator arm, illustrate our approach.

Keywords:Delay systems, Stability of nonlinear systems;Switched systems Abstract: A control design algorithm for hyper exponential stabilization of multi-input multi-output linear control system with state-delays is presented based on method of Implicit Lyapunov-Krasovskii Functional (ILKF). The procedure of control parameters tuning is formalized by means of Linear Matrix Inequalities (LMIs). The theoretical results are supported with numerical simulations.

Keywords:Delay systems, Observers for nonlinear systems Abstract: For a particular family of systems, we construct observers in the case where the measured variables are affected by the presence of a point-wise time-varying delay. The key feature of the proposed observers is that the size of their gains is proportional to the inverse of the largest value taken by the delay. The main result is first presented in the case of linear chain of integrators and next is extended to nonlinear systems with specific nonlinearities. Numerical examples are provided to show the validity and effectiveness of the proposed observers.

Keywords:Delay systems, PID control, Uncertain systems Abstract: This paper concerns the delay margin achievable using PID controllers for linear time-invariant (LTI) systems subject to variable, unknown time delays. The basic issue under investigation addresses the question: What is the largest range of time delay so that there exists a single PID controller to stabilize the delay plants within the entire range? Delay margin is a fundamental measure of robust stabilization against uncertain time delays and poses a fundamental, longstanding problem that remains open except in simple, isolated cases. In this paper we develop explicit expressions of the exact delay margin and its upper bounds achievable by a PID controller for low-order delay systems, notably the first- and secondorder unstable systems with unknown delay. The effect of nonminimum phase zeros is also examined. PID controllers have been extensively used to control industrial processes which are typically modeled by first- and second-order dynamics. Our results herein should provide useful guidelines in tuning PID controllers and in particular, the fundamental limits of delay within which a PID controller may robustly stabilize the delay processes.

Keywords:Delay systems, Nonlinear output feedback, Stability of nonlinear systems Abstract: The challenging problem of output feedback control of nonlinear systems with a long input delay is considered. The controller includes a new state predictor with a chain of predictors. The chain structure of the predictor enables the controller to compensate for long input delays. Contrary to the conventional predictor-based methods, the proposed control method contains no integral term and needs no approximation for its implementation. Also, it is the first time that an output feedback controller is designed for nonlinear systems with a long input delay. The stability of the controller is shown using a Lyapunov-Krasovskii functional, linear matrix inequalities and with an assumption of input-to-state stability of the system. The design procedure is illustrated by a computer simulation.

Keywords:Delay systems, Uncertain systems, Observers for nonlinear systems Abstract: This paper deals with asymptotic stabilization of a class of nonlinear input-delayed systems via dynamic output-feedback in the presence of constant disturbances. The proposed strategy has the structure of an observer-based control law. However, the observer estimates and predicts both the plant and the external disturbance, being the latter counteracted by feed-forward. A nominal delay value is assumed to be known and stability conditions in terms of linear matrix inequalities are derived for fast-varying delay uncertainties. The controller design problem is also addressed and a numerical example is provided to illustrate the usefulness of the proposed strategy.

Keywords:Delay systems, Uncertain systems, Time-varying systems Abstract: In a 2016 IEEE Conference on Decision and Control paper, our team designed sequential predictors for time-varying linear systems with time-varying delays, to prove global exponential stabilization properties using a feedback control that is computed in terms of the state of the last sequential predictor. This allowed feedback delays of arbitrarily large sup norm in the original system. Here we provide a significant generalization to more challenging cases with arbitrarily large feedback delay bounds, and where, in addition, current values of the plant state are not available to use in the sequential predictors. We illustrate our work in a pendulum example.

Keywords:Optimization algorithms, Predictive control for linear systems, Computational methods Abstract: In a range of applications, model predictive control (MPC) is implemented on embedded devices, motivating the use of conceptually and computationally simple optimization techniques. The alternating direction method of multipliers (ADMM) is a promising candidate for such situations, since it relies on simple algebraic operations and shows large potential for problem-specific adaption. In this paper, we exploit structure in the controlled system by introducing virtual subsystems, which make it possible to customize ADMM for utilizing this structure. When applied to an appropriate system, the resulting algorithm shows (i) reduced computational cost (ii) the opportunity for parallelization (iii) better scalability, and (iv) overall improved convergence performance.

Keywords:Optimization algorithms, Predictive control for nonlinear systems;Power systems Abstract: In this paper, we compare the performance of Bernstein global optimization algorithm based nonlinear model predictive control (NMPC) with a power system stabilizer and linear model predictive control (MPC) for the excitation control of a single machine infinite bus power system. The control simulation studies with Bernstein algorithm based NMPC show improvement in the system damping and settling time when compared with respect to a power system stabilizer and linear MPC scheme. Further, the efficacy of the Bernstein algorithm is also compared with global optimization solver BMIBNB from YALMIP toolbox in terms of NMPC scheme and results are found to be satisfactory.

Keywords:Optimization algorithms, Stability of nonlinear systems, Lyapunov methods Abstract: This paper introduces an efficient first-order method based on the alternating direction method of multipliers (ADMM) to solve semidefinite programs (SDPs) arising from sum-of-squares (SOS) programming. We exploit the sparsity of the coefficient matching conditions when SOS programs are formulated in the usual monomial basis to reduce the computational cost of the ADMM algorithm. Each iteration of our algorithm requires one projection onto the positive semidefinite cone and the solution of multiple quadratic programs with closed-form solutions free of any matrix inversion. Our techniques are implemented in the open-source MATLAB solver SOSADMM. Numerical experiments on SOS problems arising from unconstrained polynomial minimization and from Lyapunov stability analysis for polynomial systems show speed-ups compared to the interior-point solver SeDuMi, and the first-order solver CDCS.

Keywords:Optimization algorithms, Optimization Abstract: In this paper, we analyze first-order methods to find a KKT point of the nonlinear optimization problems arising in Model Predictive Control (MPC). The methods are based on a projected gradient and constraint linearization approach, that is, every iteration is a gradient step, projected onto a linearization of the constraints around the current iterate. We introduce an approach that uses a simple lp merit function, which has the computational advantage of not requiring any estimate of the dual variables and keeping the penalty parameter bounded. We then prove global convergence of the proposed method to a KKT point of the nonlinear problem. The first-order methods can be readily implemented in practice via the novel tool FalcOpt. The performance is then illustrated on numerical examples and compared with conventional methods.

Keywords:Predictive control for nonlinear systems, Optimization algorithms, Computational methods Abstract: In recent years several fast Nonlinear Model Predictive Control (NMPC) strategies have been proposed, aiming at reducing computational burden and widening the scope of NMPC techniques. A promising approach is the Real-Time Iteration (RTI) scheme, where Nonlinear Programming (NLP) problems are parametrized by multiple shooting and only one Sequential Quadratic Programming (SQP) iteration is performed at every sampling instant. A computationally expensive step of RTI is the calculation of sensitivity information of nonlinear dynamics, especially for problems with large system dimensions or long prediction horizons. In this paper, an inexact sensitivity updating scheme to be used in the RTI framework is proposed, that allows to reduce the number of sensitivities updates over the prediction horizon at each sampling instant. A Curvature-like Measure of Nonlinearity (CMoN) of dynamic systems is used as a metric to quantify the linearization reliability, and to trigger sensitivity update only if needed. Numerical simulation results show that the proposed approach can significantly reduce the on-line computational efforts for sensitivity computations without major impact on the control performance.

Keywords:Predictive control for nonlinear systems, Optimization algorithms, Optimal control Abstract: In this paper, a strategy is proposed to reduce the computational burden associated with the solution of problems arising in nonlinear model predictive control. The prediction horizon is split into two sections and the constraints associated with the terminal one are tightened using a barrier formulation. In this way, when using the Real-Time Iteration scheme, variables associated with such stages can be efficiently eliminated from the quadratic subproblems by a single backward Riccati sweep. After eliminating the tightened stages, a quadratic problem with a reduced horizon is solved where the original constraints are used. The solution is then expanded to the full horizon with a single forward Riccati sweep. By doing so, the online computational burden associated with the solution of the optimization problems can be largely reduced. Numerical results are reported where, using the proposed scheme, a speedup of about one order of magnitude can be achieved without compromising closed-loop performance.

Keywords:Networked control systems, Boolean control networks and logic networks, Network analysis and control Abstract: Cactus graphs, which are connected graphs with no edge contained in two or more different simple cycles, are known to provide useful properties for network systems. Meanwhile, it has been found that a non-cactus graph can be equivalently transformed into a cactus graph by using a conventional technique for simplifying block diagrams; however, the condition under which such transformation is possible has never been clarified so far. This paper addresses the problem of finding graphs which can be transformed into a cactus graph. As a solution, we present a sufficient condition based on two graph characteristics, called the nucleus graphs and doubly bidirectionally-connected pairs.

Keywords:Biomolecular systems, Network analysis and control, Systems biology Abstract: We consider flow-inducing networks, a class of models that are well-suited to describe important biochemical systems, including the MAPK pathway and the interactions at the trans-Golgi network. A flow-inducing network is given by the interconnection of subsystems (modules), each associated with a stochastic state matrix whose entries depend on the state variables of other modules. This results in an overall nonlinear system; when the interactions are modelled as mass action kinetics, the overall system is bilinear. We provide preliminary results concerning the existence of single or multiple equilibria and their positivity. We also show that instability phenomena are possible and that entropy is not a suitable Lyapunov function. The simplest non-trivial module is the duet, a second order system whose variables represent the concentrations of a species in its activated and inhibited state: under mass action kinetics assumptions, we prove that (i) a negative loop of duets has a unique equilibrium that is unconditionally stable and (ii) a positive loop of duets has either a unique stable equilibrium on the boundary or two equilibria, of which one is unstable on the boundary and one is strictly positive and stable; both properties (i) and (ii) hold regardless of the number of duets in the loop.

Keywords:Boolean control networks and logic networks, Network analysis and control, Genetic regulatory systems Abstract: A conjunctive Boolean network (CBN) is a discrete-time finite state dynamical system, whose variables take values from a binary set, and the value update rule for each variable is a Boolean function consisting only of logic AND operations. Since a CBN is a finite state dynamical system, every trajectory generated by the system will enter a periodic orbit. We characterize in this paper the asymptotic behavior of a special class of weakly connected CBNs where the strongly connected components of their dependency graphs are all cycles of positive lengths. Given an initial condition of such a CBN, we characterize a periodic orbit which the system enters with the given initial condition.

Keywords:Cooperative control, Agents-based systems, Stability of nonlinear systems Abstract: Cyclic pursuit frameworks provide an efficient way to create useful global behaviors out of pairwise interactions in a collective of autonomous robots. Earlier work studied cyclic pursuit with a constant bearing (CB) pursuit law, and has demonstrated the existence of a variety of interesting behaviors for the corresponding dynamics. In this work, by attaching multiple branches to a single cycle, we introduce a modified version of this framework which allows us to consider any weakly connected pursuit graph where each node has an outdegree of 1. This provides a further generalization of the cyclic pursuit setting. Then, after showing existence of relative equilibria (rectilinear or circling motion), pure shape equilibria (spiraling motion) and periodic orbits, we also derive necessary conditions for stability of a 3-agent collective. By paving a way for individual agents to join or leave a collective without perturbing the motion of others, our approach leads to improved reliability of the overall system.

Keywords:Network analysis and control, Control system architecture Abstract: There has been recent growing interest in graph theoretical properties known as r- and (r,s)-robustness. These properties serve as sufficient conditions guaranteeing the success of certain consensus algorithms in networks with misbehaving agents present. Due to the complexity of determining the robustness for an arbitrary graph, several methods have previously been proposed for identifying the robustness of specific classes of graphs or constructing graphs with specified robustness levels. The majority of such approaches have focused on undirected graphs. In this paper we identify a class of scalable directed graphs whose edge set is determined by a parameter k and prove that the robustness of these graphs is also determined by k. We support our results through computer simulations.

Keywords:Traffic control;Smart cities/houses, Transportation networks Abstract: In prior work, we addressed the problem of optimally controlling on line connected and automated vehicles crossing two adjacent intersections in an urban area to minimize fuel consumption while achieving maximal throughput without any explicit traffic signaling and without considering left and right turns. In this paper, we extend the solution of this problem to account for left and right turns under hard safety constraints. Furthermore, we formulate and solve another optimization problem to minimize a measure of passenger discomfort while the vehicle turns at the intersection and we investigate the associated tradeoff between minimizing fuel consumption and passenger discomfort.

Keywords:Optimization;Energy systems, Stochastic systems Abstract: The development of renewable energy has been recognized as a promising resolution to fuel depletion and excess carbon emission. However, the utilization of renewable energy is far less than satisfactory due to the inherent uncertainty. The rapid development of electric vehicles (EVs) provides new opportunities to balance volatile renewable generation. Nowadays, modern technology advances allow to mount on-site wind power generators on the buildings. Considering that EVs are usually parked in buildings, the problem to coordinate EV charging with locally generated wind power of buildings shows various significance. Therefore, we investigate this important problem and three contributions are made. First, we formulated it as an EV-based multiagent Markov decision process (MMDP), which incorporates the uncertain wind power supply at different buildings and the random driving requirements of EVs. Second, to alleviate curses of dimensionality associated with the number of EVs, we developed an EV aggregation framework, which dynamically groups EVs into EVAs (electric vehicle aggregator) based on their remaining parking time and locations. And an EVA-based MMDP is derived. Third, scenario-tree based dynamic programming (TSP) is introduced to incorporate the multiple uncertainties in the problem. And the performance of this method is demonstrated by a number of case studies.

Keywords:Smart cities/houses, Identification, Linear systems Abstract: Identifying building thermal model with communication imperfections (data -loss and -corruption) is emerging as a major challenge in deploying Internet of Things (IoT) based building automation systems. Further, the building thermal model is influenced by multiple inputs cooling energy, stray heating, and weather, leading to a multi-input and single output (MISO) system, compounding the challenge further. This investigation presents an approach for identifying high fidelity, yet simple building thermal model suitable for designing predictive controllers for heating, ventilation and air-conditioning systems with IoT induced imperfections. By construction, the problem of finding the lowest order MISO model is a cardinality optimization problem, known to be non-convex and NP-hard. To solve this problem, we first define an atomic norm suitable to relax the cardinality reduction problem for simplifying the identification. Then the resulting problem is solved by employing a randomized version of the Frank-Wolfe algorithm. The performance of the proposed identification algorithm is illustrated on a MISO building thermal model. Our results show that the proposed approach is more suitable for identifying the lowest order building thermal models with missing and corrupted data due to the network.

Keywords:Machine learning;Smart cities/houses;Energy systems Abstract: Decisions on how to best operate large complex plants such as natural gas processing, oil refineries, and energy efficient buildings are becoming ever so complex that model-based predictive control (MPC) algorithms must play an important role. However, a key factor prohibiting the widespread adoption of MPC, is the cost, time, and effort associated with learning first-principles dynamical models of the underlying physical system. An alternative approach is to employ learning algorithms to build black-box models which rely only on real-time data from the sensors. Machine learning is widely used for regression and classification, but thus far data-driven models have not been used for closed-loop control. We present novel Data Predictive Control (DPC) algorithms that use Regression Trees and Random Forests for receding horizon control. We demonstrate the strength of our approach with a case study on a bilinear building model identified using real weather data and sensor measurements. We further apply DPC to a large scale multi-story EnergyPlus building model to curtail total power consumption in a Demand Response setting. In such cases, when the model-based controllers fail due to modeling cost, complexity and scalability, our results show that DPC curtails the desired power usage with high confidence.

Keywords:Autonomous robots, Optimization;Smart cities/houses Abstract: In this paper, we study the control and communication co-design for networked vehicles that coordinate with each other to achieve safe operations. We propose a control-theoretical framework for distributed motion planning for multi-agent such that complex and high-level specifications are satisfied while communication quality is optimized. The desired motion specifications and communication performance are specified as signal temporal logic (STL) and spatial-temporal logic (SpaTeL) formulas, respectively. We encode the formulas as the constraints in mixed integer linear programs (MILP), and upon which control strategies satisfying both STL and SpaTeL specifications are generated locally by employing a distributed model predictive control (MPC) framework. The effectiveness of the proposed framework is validated by simulation.

Keywords:Smart cities/houses, Hierarchical control, Optimization Abstract: Modern transportation system suffers from increasing passenger demand, limited vehicle supply and inefficient mobility service. Towards building an intelligent transportation system to address these issues, we propose a hierarchical framework to implement strategies that is capable of allocating vehicles to serve passengers in different locations based on limited supply. In the higher hierarchy, we optimize idle mileage induced by rebalancing vehicles across regions using receding horizon control towards current and predicted future requests. In addition, we design a dispatch strategy that is robust against passenger demand and vehicle mobility pattern uncertainties. In the lower hierarchy, within each region, pick-up and drop-off schedules for real-time requests are obtained for each vehicle by solving mixed-integer linear programs (MILP). The objective of the MILP is to minimize total mileage delay due to ride-sharing while serving as many requests as possible. We illustrate the validity of our framework via numerical simulations on taxi trip data from New York City.

Keywords:Optimization algorithms, Uncertain systems, Power systems Abstract: In this paper, we consider chance-constrained decision problems with a specific structure: on one hand, we assume that some prior information about the unknown parameters of the decision problem is known, in the form of samples; on the other hand, we assume that it is possible to gather further information regarding the true value of these parameters via measurements. We specialize the scenario approach so that the apriori samples can be efficiently used, together with the available measurement, to generate the feasible region where chance constraints are satisfied. This results in a two-phase algorithm, composed of an offline pre-processing of the samples, followed by an online part that needs to be performed as soon as the measurement is available. This online part is computationally extremely lightweight, both in terms of computation time and of memory footprint, and is therefore suited for implementation in embedded systems. As an application of choice, we consider the control of microgenerators in a power distribution grid.

Keywords:Power systems, Optimization, Stochastic systems Abstract: Dealing with the generation uncertainty that is introduced by Renewable Energy Sources (RES) may increase signicantly the operational costs of the system both due to the resulting stochastic formulation and the additional reserve requirements. In this paper, we focus on the optimal provision of reserve capacities as well as their real-time deployment. The latter can be modeled by control policies as a function of the generation-load power mismatch that is driven in our case by the generation uncertainty of RES. We also take advantange of the capability of HVDC lines to be controlled in real time and design policies for them as well. By arbitrarily increasing the complexity of those policies may not lead to significant cost benefits. We will find the optimal policy-based functions using tools from multiparametric optimization and in this way increase the feasibility space compared to the state-of the-art control. The analysis will take place within a probabilistic framework that uses scenario based optimization in order to provide a solution with a-priori performance guarantees. The theoretical results will be supported by simulation case studies on the IEEE 30-bus system which will be modified to include wind power infeeds and HVDC lines.

Keywords:Power systems, Optimization, Stochastic systems Abstract: Higher shares of electricity generation from renewable energy sources has led to larger fluctuations and increased uncertainty in power systems operation. To enable secure operation, several methods have been proposed that allow the power system operator to plan operation such that the risk of constraint violation is limited to below a pre-defined acceptable value.

Reducing risk not only increases system security, but also the operational cost. Therefore, the risk limit must be chosen carefully to reflect an appropriate trade-off between cost and security. In this paper, we propose a stochastic optimal power flow framework that co-optimizes the risk limits and the generation dispatch, and achieves an optimal balance between risk reduction and cost of operation. We demonstrate the method through an implementation on IEEE test cases.

Keywords:Smart grid;Power systems;Power generation Abstract: Primary frequency response is provided by synchronized generators through their speed-droop governor characteristic in response to instant frequency deviations that exceed a certain threshold, also known as the governor dead zone. This dead zone makes the speed-droop governor characteristic nonlinear and makes affine control policies that are often used for modeling the primary frequency response inaccurate. This paper presents an Optimal Power Flow (OPF) formulation that explicitly models i) the primary frequency response constraints using a nonlinear speed-droop governor characteristic and ii) chance constraints on power outputs of conventional generators and line flows imposed by the uncertainty of renewable generation resources. The proposed Chance Constrained OPF (CCOPF) formulation with primary frequency response constraints is evaluated and compared to the standard CCOPF formulation on a modification of the 118- bus IEEE Reliability Test System.

Keywords:Smart grid, Stochastic systems;Power systems Abstract: The increasing penetration of renewable energy sources to power grids necessitates a structured consideration of uncertainties for optimal power ﬂow problems. Modeling uncertainties via continuous random variables of ﬁnite variance we propose a tractable convex formulation of the uncertain optimal power ﬂow problem. The uncertainties can be (non-)Gaussian, multivariate and/or correlated. We employ polynomial chaos expansion to rewrite the inﬁnite-dimensional random-variable optimization problem as a ﬁnite-dimensional convex second-order cone program. This problem can be solved efﬁciently in a single numerical run for all realizations of the uncertainty. The solution provides a feedback policy in terms of the ﬂuctuations. No Monte Carlo sampling is required to obtain either the solution or its statistics. The reduced computational effort and yet consistent results stemming from polynomial chaos are demonstrated in comparison to a Monte-Carlo-based solution for the IEEE 300-bus test system.

Keywords:Power systems;Smart grid, Optimization Abstract: In this paper we address the problem of optimal sizing of a given number of energy storage systems in a distribution network. These devices represent an effective solution for distribution system operators to prevent over- and undervoltages in distribution feeders. The sizing problem is first formulated in a two-stage stochastic framework in order to cope with uncertainty on future demand and distributed generation. By taking a scenario-based approach, this problem is then approximated in the form of a multi-scenario, multi-period optimal power flow. Since the size of the latter problem becomes rapidly prohibitive as the number of scenarios grows, a novel scenario reduction procedure is proposed. The procedure consists of solving a sequence of problems with scenario sets of increasing size. Tightness of the lower bound generated at each iteration can be checked very efficiently. Typically, a tight solution is obtained for sizes much smaller than that of the original scenario set. The algorithm is tested on the topology of a real Italian distribution network, using historical data of demand and generation to build the scenarios.

Keywords:Aerospace;Control applications, Variable-structure/sliding-mode control Abstract: For both safety and technological reasons, aircraft anti-skid control systems are typically based on the wheel speed measurement only, which yields a self-contained landing-gear subsystem, but makes the anti-skid performance sub-optimal. For a better exploitation of the available tire-runway grip, a modulating control technique is herein proposed: it ensures improved closed-loop properties but requires the estimation of the aircraft velocity, which cannot be directly measured. In this paper, an aircraft landing gear control-oriented model is presented considering the distinctive effect of the gear walk dynamics. A Mixed Slip and Deceleration anti-skid controller is introduced to suitably leverage the runway available grip. Then, a Sliding Mode Observer is proposed to estimate aircraft wheel slip and, based on a non linear curve fitting procedure, it allows also to retrieve road friction conditions. Finally, simulation results of the implemented model are shown in order to quantitatively asses the combined estimation and control performance.

Keywords:Aerospace, Cooperative control Abstract: Threat-seduction in its simplest form refers to the problem of luring a threat away from an asset by employing a decoy that emits a signature that is indistinguishable from the asset. In this paper, first the problem of motion control of a single decoy to achieve successful threat seduction is studied. Second, the case where multiple decoys need to respond to multiple threats is investigated and an algorithm to minimise the worst-case response time is proposed. Numerical results are presented in the end.

Keywords:Aerospace;Hybrid systems, Observers for nonlinear systems Abstract: This paper focuses on attitude tracking control of rigid body with global asymptotic stability in the absence of angular velocity information. First of all, Lyapunov transformation is used to cancel quadratic term on angular velocity in the equation. Based upon this transformation, a hybrid observer is proposed to estimate angular momentum in the inertia frame, and the estimate of angular velocity can be reconstructed by the estimate of angular momentum. Then a backstepping-based hybrid attitude controller is derived, where an auxiliary variable is introduced to guarantee global asymptotic stability of closed-loop system.

Keywords:Aerospace, Predictive control for linear systems, Uncertain systems Abstract: This paper considers the problem of designing a control strategy for proximity operations of space systems. The considered setup is realistic, and is based on a model derived and validated on an experimental test-bed. Parametric uncertainties due to the mass variations during operations, linearization errors, and disturbances due to external space environment are simultaneously considered. The proposed control strategy is based on a novel framework for stochastic model predictive control (SMPC), extending the results based on offline sampling strategies previously developed.

Keywords:Aerospace, Stability of nonlinear systems Abstract: This paper addresses the coupled rotation and translation control problem for final phase proximity operations of spacecraft autonomous rendezvous and docking. During the proximity phase, some important motion constraints are considered. On the one hand, to track the target spacecraft, the sensor system of the chaser spacecraft is required to continuously point toward the target; on the other hand, for safety concerns, the chaser also needs to satisfy approaching path constraints. A special dual-quaternion based artificial potential function (APF) is presented to encode information regarding these motion constraints, and then a novel six-degree-of-freedom control method is proposed to ensure the chaser can finally arrive at the docking port of the target with the desired attitude while strictly complying with all specified constraints. Stability of the closed-loop system is demonstrated by the Lyapunov-based method along with the convex property of the proposed APF. Simulation results of a prototypical spacecraft rendezvous and docking mission are provided to illustrate the effectiveness of the proposed method.

Acad. of Mathematics and Systems Science, ChineseAcademyof Scie

Keywords:Autonomous systems;Aerospace, Distributed control Abstract: This paper investigates the attitude synchronization problem of the dynamical model of a group of moving rigid bodies in SE(3). Two rigid bodies are said to be neighbors if their distance is less than a pre-defined interaction radius. We design the control laws for the torque and the force of the rigid bodies using the potential function method, and prove that under some conditions on the initial states of the rigid bodies, the neighbor graphs keep connected, and thus the rigid body system reaches the attitude synchronization and no collision occurs. The numerical simulations demonstrate the feasibility of the proposal control schemes.

Keywords:Control over communications, Stochastic systems;Stability of hybrid systems Abstract: In this paper, we study the problem of computing the minimum battery capacity required to stabilize a scalar plant communicating with an energy harvesting sensor over a wireless communication channel. We prove that a particular greedy battery management policy suffices to stabilize the plant, and demonstrate that stability of the system under the greedy policy can be checked by a linear program. Moreover, we show that a critical battery capacity exists, below which no policy can stabilize the system, which itself can be computed by solving a sequence of linear programs which grows logarithmically with respect to the maximum allowed storage capacity. The first of these results address an open question pertaining to the stability of energy harvesting control systems. The last allows us to efficiently compute the smallest battery capacity required to stabilize a given system, which addresses a problem of importance when device size or cost are significant concerns.

Keywords:Distributed control, Control over communications, Large-scale systems Abstract: This paper studies problems on locally stopping distributed consensus algorithms over networks where each node updates its state by interacting with its neighbors and decides by itself whether certain level of agreement has been achieved among nodes. Since an individual node is unable to access the states of those beyond its neighbors, this problem becomes challenging. In this work, we first define the stopping problem for generic distributed algorithms. Then, a distributed algorithm is explicitly provided for each node to stop consensus updating by exploring the relationship between the so-called local and global consensus. Finally, we show both in theory and simulation that its effectiveness depends both on the network size and the structure.

Keywords:Control over communications, Linear systems, Control of networks Abstract: This paper considers the distributed event-triggered consensus problem for multi-agent systems with general linear dynamics under a directed graph. We propose a novel distributed event-triggered consensus controller with state-dependent threshold for each agent to achieve consensus, without continuous communication in either controller update or triggering condition monitoring. Each agent only needs to monitor its own state continuously to determine if the event is triggered. Additionally, the approach shown here provides consensus with guaranteed positive inter-event time intervals. Therefore, there is no Zeno behavior under the proposed consensus control algorithm. Finally, numerical simulations are given to illustrate the theoretical results.

Keywords:Control over communications, Linear systems, Control of networks Abstract: This paper studies the event-triggered output consensus problem of heterogeneous multi-agent systems with general linear dynamics under an directed graph. With the state-dependent triggering function, we design a novel distributed event-triggered output consensus controller for each agent to reach consensus with zero final consensus error. This strategy has several distinguishing features, including the fact that individual agent does not require continuous, or even periodic, communication with their neighbors to update the controller or monitor the triggering condition, and all parameters required by its implementation can be locally determined by the agent. We also prove that events cannot be triggered infinitely in finite time (i.e. no Zeno behavior). Furthermore, the simulation examples are given to illustrate the theoretical analysis.

Keywords:Control over communications, Stochastic optimal control, Modeling Abstract: Motion modulating communications provide a number of potential benefits in low cost, size, weight and power as well as offering stealthy modes for secure communications. This paper describes a motion modulation design for achieving symbol distinguishability and energy efficiency, which is further subject to technical risk management due to performance uncertainties and distributions. The basic concepts underlying the approach to optimal motion modulating communication design include: (i) a vector modulation control for use in analog communication systems; (ii) incorporation of a linear dynamical system whose noisy output is the actual modulating motion; and (iii) the Kalman-Bucy filter for motion demodulation.

Keywords:Agents-based systems, Distributed control, Control over communications Abstract: A digraph with positive integer weights on its (directed) edges is weight-balanced if, for each node, the sum of the weights of the incoming edges equals the sum of the weights of the outgoing edges. We develop a distributed iterative algorithm in which nodes are in charge of updating the weights on their outgoing edges based on certain values they maintain and update, using corresponding information available from their immediate in- and out-neighbors. We assume that communication between neighboring nodes is bidirectional, but unreliable in that it may result in possible packet drops, independently between different links and link directions. We show that, even when communication links drop packets occasionally (but not always) and the integer weights are constrained to be within an interval, captured by lower and upper limits, the proposed algorithm allows nodes to reach integer weight balancing after a finite number of iterations with probability one, as long as the necessary and sufficient circulation conditions on the lower and upper edge weight limits are satisfied. We also provide examples to illustrate the operation and performance of the proposed algorithm.

Keywords:Game theory, Communication networks, Sensor networks Abstract: This paper studies signaling games in cyberphysical systems with strategic components. The communication network of a cyber-physical system is modeled as a sensor network, which involves a single Gaussian state observed by many sensors, subject to additive independent Gaussian observation noises. The sensors communicate with the receiver over a coherent Gaussian multiple access channel. There are two groups of sensors–strategic and non-strategic. The common objective of the team of non-strategic sensors and the receiver is to reconstruct the underlying state with minimum mean squared error. The team of strategic sensors, on the other hand, strives to minimize a different distortion function, which depends on the state, the reconstruction at the receiver and the type (bias) variable–an independent random variable whose realization is available only to the strategic sensors. It is shown that the ability of the team of non-strategic sensors and the receiver to secretly agree on a random event, that is “coordination”, plays a key role in the analysis. The properties and scaling behavior of the Stackelberg equilibrium of this signaling game are analyzed, in conjunction with the set of affine communication strategies, depending on the aforementioned coordination capability.

Keywords:Machine learning, Game theory, Communication networks Abstract: With a large number of sensors and control units in networked systems, distributed support vector machines (DSVMs) play a fundamental role in scalable and efficient multi-sensor classification and prediction tasks. However, DSVMs are vulnerable to adversaries who can modify and generate data to deceive the system to misclassification and misprediction. This work aims to design defense strategies for DSVM learner against a potential adversary. We use a game-theoretic framework to capture the conflicting interests between the DSVM learner and the attacker. The Nash equilibrium of the game allows predicting the outcome of learning algorithms in adversarial environments, and enhancing the resilience of the machine learning through dynamic distributed algorithms. We develop a secure and resilient DSVM algorithm with rejection method, and show its resiliency against adversary with numerical experiments.

Keywords:Optimization algorithms;Information theory and control, Communication networks Abstract: This paper studies the delay-accuracy trade-off for an unconstrained quadratic Network Utility Maximization (NUM) problem, which is solved by a distributed, consensus based, constant step- size, gradient-descent algorithm. Information theoretic tools such as entropy power inequality are used to analyse the convergence rate of the algorithm under quantised inter-agent communication. A finite- time distributed algorithm is proposed to solve the problem under synchronised message passing. For a system with N agents, the algorithm reaches any desired accuracy within 2N iterations, by adjusting the step-size, α. However, if N is quite large or if the agents are constrained by their memory or computational capacities, asymptotic convergence algorithms are preferred to arrive within a permissible neighbourhood of the optimal solution. The analytical tools and algorithms developed shed light to delay- accuracy trade-off required for many real-time IoT applications, e.g., smart traffic control and smart grid. As an illustrative example, we use our algorithm to implement an intersection management application, where distributed computation and communication capabilities of smart vehicles and road side units increase the efficiency of an intersection.

Keywords:Game theory, Stochastic systems, Decentralized control Abstract: We consider a non-cooperative multi-stage game with discrete-time state dynamics. Players have their own decoupled state dynamics and each player wishes to minimize its own expected total cost. The salient aspect of our model is that each player's stage cost includes a payment (e.g., to a public utility) proportional to the magnitude of the player's decision. The coefficient multiplying each player's decision, called the price, is the same for all players and is determined as a function of the average of all player's decisions at that stage. Hence, each player's cost depends on the decisions of the other players only through the price. Here, we provide a stochastic and dynamic generalization of an equilibrium concept adopted in the economics literature, called the price-taking equilibrium, at which each player has no incentive to unilaterally deviate from its equilibrium strategy provided that the player ignores the effect of its own decisions on the price. In our setup, we allow for stochasticity in the price process and players observe only the past price realizations in addition to their own state realizations and their own past decisions. At a price-taking equilibrium, if players are given the distribution of the price process as if the price process is exogenous, they would have no incentive to unilaterally deviate from their equilibrium strategies. The main contribution of this paper is to establish such a stochastic and dynamic game generalization of price taking equilibria. We first derive the conditions for the existence of a price-taking equilibrium in the special case where the state dynamics are linear, the stage cost are quadratic, and the price function is linear. In this special case, our existence results are constructive for both finite-horizon and infinite horizon-problems. In the case where the number of players is taken to infinity, a price taking equilibrium exists which in turn is a mean-field equilibrium and is thus actually a Bayesian Nash equilibrium unlike the setup with a finite number of players. Finally, non-constructive existence results for price-taking equilibria and asymptotic equivalence with Nash equilibria are obtained for the case where the state and action sets are finite.

Keywords:Agents-based systems, Game theory, Control of networks Abstract: In this paper we consider the problem of distributed Nash equilibrium (NE) seeking for a class of games over networks, a setting in which players have limited local information. We start from a continuous-time gradient-play dynamics that converges to an NE under strict monotonicity of the pseudo-gradient and assumes perfect information, i.e., instantaneous all-to-all player communication. We consider how to modify this gradient-play dynamics in the case of partial, or networked information between players. We propose an augmented gradient-play dynamics with correction in which players communicate locally only with their neighbours to compute an estimate of the other players' actions. We derive the new dynamics based on the reformulation as a multi-agent coordination problem over an undirected graph. We exploit incremental passivity properties and show that a synchronizing, distributed Laplacian feedback can be designed using relative estimates of the neighbours. Under a strict monotonicity property of the pseudo-gradient, we show that the augmented gradient-play dynamics converges to the consensus subspace, and moreover the action components converge to an NE of the game. We further discuss two cases that highlight the tradeoff between properties of the game and the communication graph.

Keywords:Game theory, Network analysis and control Abstract: This paper studies network design and efficiency loss in online platforms using the model of networked Cournot competition. We consider two styles of platforms: open access platforms and discriminatory access platforms. In open access platforms, every firm can connect to every market, while discriminatory access platforms limit connections between firms and markets in order to improve social welfare. Our results provide tight bounds on the efficiency loss of both open access and discriminatory access platforms. For open access platforms, we show that the efficiency loss at a Nash equilibrium is upper bounded by 3/2. In the case of discriminatory access platforms, we prove that, under an assumption on the linearity of cost functions, a greedy algorithm for optimizing network connections can guarantee the efficiency loss at a Nash equilibrium is upper bounded by 4/3.

Keywords:Identification, Estimation Abstract: Finite-sample system identification algorithms can be used to build guaranteed confidence regions for unknown model parameters under mild statistical assumptions. It has been shown that in many circumstances these rigorously built regions are comparable in size and shape to those that could be built by resorting to the asymptotic theory. The latter sets are, however, not guaranteed for finite samples and can sometimes lead to misleading results. The general principles behind finite-sample methods make them virtually applicable to a large variety of even nonlinear systems. While these principles are simple enough, a rigorous treatment of the attendant technical issues makes the corresponding theory complex and not easy to access. This is believed to be one of the reasons why these methods have not yet received widespread acceptance by the identification community and this letter is meant to provide an easy access point to finite-sample system identification by presenting the fundamental ideas underlying these methods in a simplified manner. We then review three (classes of) methods that have been proposed so far — 1) Leave-out Sign-dominant Correlation Regions (LSCR); 2) Sign-Perturbed Sums (SPS); 3) Perturbed Dataset Methods (PDMs). By identifying some difficulties inherent in these methods, we also propose in this letter a new sign-perturbation method based on correlation which overcome some of these difficulties.

Keywords:Identification, Estimation, Time-varying systems Abstract: The problem of parametric, autoregressive model based estimation of a time-varying spectral density function of a nonstationary process is considered. It is shown that estimation results can be considerably improved if identification of the autoregressive model is carried out using the two-sided doubly exponentially weighted lattice algorithm which combines results yielded by two one-sided lattice algorithms running forward in time and backward in time, respectively. It is also shown that the model order and the most appropriate estimation bandwidth can be efficiently selected using the suitably modified Akaike's final prediction error criterion.

Keywords:Identification, Linear parameter-varying systems Abstract: This article presents an optimal estimator for discrete-time systems disturbed by output white noise, where the proposed algorithm identifies the parameters of a Multiple Input Single Output LPV State Space model. This is an LPV version of a class of algorithms proposed elsewhere for identifying LTI systems. These algorithms use the matchable observable linear identification parameterization that leads to an LTI predictor in a linear regression form, where the ouput prediction is a linear function of the unknown parameters. With a proper choice of the predictor parameters, the optimal prediction error estimator can be approximated. In a previous work, an LPV version of this method, that also used an LTI predictor, was proposed; this LTI predictor was in a linear regression formenablin, in this way, the model estimation to be handled by a Least--Squares Support Vector Machine approach, where the kernel functions had to be filtered by an LTI 2D-system with the predictor dynamics. As a result, it can never approximate an optimal LPV predictor which is essential for an optimal prediction error LPV estimator. In this work, both the unknown parameters and the state-matrix of the output predictor are described as a linear combination of a finite number of basis functions of the scheduling signal; the LPV predictor is derived and it is shown to be also in the regression form, allowing the unknown parameters to be estimated by a simple linear least squares method. Due to the LPV nature of the predictor, a proper choice of its parameters can lead to the formulation of an optimal prediction error LPV estimator. Simulated examples are used to assess the effectiveness of the algorithm. In future work, optimal prediction error estimators will be derived for more general disturbances and the LPV predictor will be used in the Least--Squares Support Vector Machine approach.

Keywords:Identification, Numerical algorithms, Machine learning Abstract: We study the problem of estimating the largest gain of an unknown linear and time-invariant filter, which is also known as the H_infinity norm of the system. By using ideas from the stochastic multi-armed bandit framework, we present a new algorithm that sequentially designs an input signal in order to estimate this quantity by means of input-output data. The algorithm is shown empirically to beat an asymptotically optimal method, known as Thompson Sampling, in the sense of its cumulative regret function. Finally, for a general class of algorithms, a lower bound on the performance of finding the H_infinity norm is derived.

Keywords:Identification, Subspace methods Abstract: In this paper, an approach to recursive subspace identification based on the coordinate-free framework of subspace identification is proposed. Herein, the predictor space serves as a natural basis for the formulation of a recursive approach. Compressing the necessary past information, the predictor space of a previous identification will be used for subsequent identifications. Implementational difficulties, which all recursive methods are faced with and hampering an accurate tracking of fast changes of the system, are also addressed.

Keywords:Identification Abstract: Abstract—Algorithms for online system identification consist in updating the estimated model while data are being collected. A standard method for online identification of structured models is the recursive prediction error method (PEM). The problem is that PEM does not have an exact recursive formulation, and the need to rely on approximations makes recursive PEM prone to convergence problems. In this paper, we propose a recursive implementation of weighted null-space fitting, an asymptotically efficient method for identification of structured models. Consisting only of (weighted) least-squares steps, the recursive version of the algorithm has the same convergence and statistical properties of the off-line version. We illustrate these properties with a simulation study, where the proposed algorithm always attains the performance of the off-line version, while recursive PEM often fails to converge.

Keywords:Cooperative control, Optimization algorithms, Distributed control Abstract: This paper proposes a novel distributed multi-step subgradient method under a common constraint set with switching undirected graphs. In the proposed method, each agent has a state and a momentum variable as the estimate of an optimal solution and the accumulated information of past gradients of neighbor agents. Similar to a momentum term in the classical momentum-based gradient algorithms, the momentum variable works as inertial force and accelerates convergence rate. We show that the states of all agents asymptotically converge to one of the optimal solutions of the convex optimization problem. The simulation results show that the proposed multi-step subgradient algorithm achieves faster convergence than the standard subgradient algorithms.

Keywords:Cooperative control, Networked control systems;Robotics Abstract: In this work a decentralized control strategy for tightly connected networked Lagrangian systems is designed. The main characteristics of the solution is that it allows to both control the motion of the handled object and the squeezing wrenches arising on it and this is achieved by resorting to a layered architecture. At the top layer, agents exploit consensus theory to distributedly estimate the full state of the system and the object dynamics to estimate the squeezing wrenches, while, at the second layer, a local adaptive control law is specified in order to both control the local contribute to the squeezing wrenches and the local motion of the robot. The effectiveness of the solution is proven by employing 6-DOFs serial chain manipulators mounted on a mobile platform to perform a cooperative load transportation task.

Keywords:Cooperative control Abstract: The objective in this article is to develop a control strategy for coverage purposes of a convex region by a fleet of Mobile Aerial Agents (MAAs). Each MAA is equipped with a downward facing camera that senses a convex portion of the area while its altitude flight is constrained. Rather than relying on typical Voronoi-like tessellations of the area to be covered, a scheme focusing on the assignment to each MAA of certain parts of the mosaic of the current covered area is proposed. A gradient ascent algorithm is then employed to increase in a monotonic manner the covered area by the MAA fleet. Simulation studies are offered to illustrate the effectiveness of the proposed scheme.

Keywords:Network analysis and control, Cooperative control, Estimation Abstract: This paper considers the problem of estimating all the eigenvalues and eigenvectors of an irreducible matrix, corresponding to a strongly connected digraph, in the absence of knowledge on the global network topology. To this end, we propose a unified distributed strategy performed by each node in the network and relies only on the local information. The key idea is to transform the nonlinear problem of computing both the eigenvalues and eigenvectors of an irreducible matrix into a linear one. Specifically, we first transform distributively the irreducible matrix into a nonsingular irreducible matrix. Each node in the network then estimates in a distributed fashion the inverse of the nonsingular matrix by solving a set of linear equations based on a consensus-type algorithm. The eigenvalues and the corresponding eigenvectors are finally computed by exploiting the relations between the eigenvalues and eigenvectors of both the inverse and the original irreducible matrices. A Numerical example is provided to demonstrate the effectiveness of the proposed distributed strategy.

Keywords:Optimization algorithms, Cooperative control;Smart grid Abstract: We deal with general composite distributed resource allocation problems over networks, which arise from many application areas such as economic dispatch, demand response and network utility maximization. Conventionally, we have to resort to centralized or parallel approaches to solve these problems. It is well known that distributed solutions are more preferable due to their robustness to component failures and great scalability to the size of the network. In this paper, we propose an efficient algorithm, termed DuSPA, to solve the above problem in a distributed way. The algorithm is developed from the dual counterpart of the above problem leveraging on certain splitting techniques. We will show that the proposed algorithm is able to find the optimal solution with a non-ergodic convergence rate of O(1/k) in terms of fixed point residual for general (potentially non-smooth) convex cost functions with a smooth constitutive term. We also apply the proposed algorithm to the well-known economic dispatch problem and compare it with the state-of-the-art to show its effectiveness.

Keywords:Output regulation, Distributed control, Cooperative control Abstract: In this paper, we study the cooperative robust output regulation problem for discrete-time linear multi-agent systems with both communication and input delays by distributed internal model approach. We first introduce the distributed internal model for discrete-time multi-agent systems with both communication and input delays. Then, we further establish the solvability conditions for the problem via both the distributed state feedback control and the distributed output feedback control.

Keywords:Sampled-data control, Systems biology;Delay systems Abstract: In this paper we deal with the problem of tracking a desired plasma glucose evolution by means of intra-venous insulin administration, for Type 2 diabetic patients exhibiting basal hyperglycemia. A nonlinear time-delay model is used to describe the glucose-insulin regulatory system, and a model-based approach is exploited in order to design a global sampled-data control law for such system. Sontag's universal formula is designed to obtain a steepest descent feedback induced by a suitable control Lyapunov-Krasovskii functional. Such a feedback is a stabilizer in the sample-and-hold sense. Furthermore, the input-to-statestability redesign method is used in order to attenuate the effects of bounded actuation disturbances and observation errors, which can appear for uncertainties in the instruments. The proposed control law depends on sampled glucose and insulin measurements. Theoretical results are validated through simulations.

Keywords:Nonlinear systems identification, Biological systems Abstract: Oculomotor tests (OMT) are administered to quantify symptoms in neurological and mental diseases. Eye movements in response to displayed visual stimuli are registered by an digital video-based eye tracker and processed. Stimuli of simple signal form, e.g. sine waves, are traditionally used in medical practice to test the performance of the oculomotor system in smooth pursuit (SP). The calculated SP gain and the phase shift at the frequency in question are then presented as the test outcome. This paper revisits the problem of quantifying the SP dynamics from eye-tracking data by means of nonlinear system identification. First, a sparse Volterra-Laguerre (VL) model is estimated from an OMT with sufficiently exciting (in frequency and amplitude) stimuli. Then the structure and initial parameter estimates of a polynomial Wiener model (WM) are obtained from the kernel estimates of the VL model. Finally, the parameter distributions of the WM are inferred by a particle filter (PF). In the proposed approach, the performance of the PF is improved by the individualized sparse model structure. Experimental data show that the latter captures the alternations in the SP dynamics due to aging and in Parkinson's disease.

Keywords:Modeling, Biological systems, Systems biology Abstract: We introduce a nonlinear bi-compartmental dynamic tumor cell and supporting vasculature volume growth model which takes into account nutrient and cell proliferation, necrosis and angiogenesis. Validation of the model requires measurement data on tumor volume during the therapy; for explicit identification of vasculature growth dynamic, in vivo measurement data on vasculature volume during the therapy are required as well. We show that the model can be used for the evaluation of drug dosage protocols.

Keywords:Biomedical;Healthcare and medical systems, Predictive control for linear systems Abstract: Patients with type 1 diabetes mellitus (T1DM) need to supply their body with insulin from external sources in order to manage their blood glucose (BG) concentration and mitigate the long-term effects of a chronically increased BG level without falling into a potentially life-threatening hypoglycemia. Doing so is challenging and a heavy burden for those patients, which led to efforts of automating (parts of) this task, most notably in Artificial Pancreas (AP) systems. In standard AP approaches a (typically constant) reference BG value is tried to be tracked as closely as possible and often leads to satisfactory results in terms of BG management. However, requiring a constant BG can be an excessive requirement. Differently from that, this paper proposes a different framework, in which the unavoidable uncertainty is modeled in probabilistic terms and the control goal is defined not in terms of proximity to a specific BG target but as keeping the risk of leaving the euglycemic range under a given threshold. The degree of freedom gained by this problem relaxation can be used for other purposes, e.g. the minimization of total insulin intake. In the current paper an AP controller based on chance-constrained Model Predictive Control (MPC) is proposed for this purpose.

Keywords:Biological systems;Switched systems, Variational methods Abstract: In this paper, we study an optimal control problem for circadian rhythm regulation. The objective of the problem is to find a lighting schedule that minimizes the time required for a subject's circadian rhythm to synchronize with a reference circadian rhythm. We previously solved this problem with a bang-off control algorithm. However, this existing solution neglects the sleep dynamics and often results in an unreasonably uncomfortable schedule (with excessive sleepiness). In this paper, we use a hybrid system model that contains both the circadian and sleep dynamics. Using variational analysis, we show that the time-optimal control problem is still a bang-off control algorithm, but from a class of algorithms that is richer than the one previously reported.

Keywords:Biomedical, Neural networks, Optimal control Abstract: Control input sequence in a hybrid neuroprosthesis that combines functional electrical stimulation (FES) and an electric motor can be optimized by a model based optimization method, like model predictive control (MPC). However, because the human muscle model is highly nonlinear, time-varying, and contains unmeasurable state variables, it is often difficult to identify the model. Therefore, a three-layer recurrence neural network (RNN) is developed in this paper, in which backpropagation through time (BPTT) is used as training technique and the internal states are used to represent the unmeasurable states. This structure shows the potential to approximate the model of the hybrid neuroprosthesis system. After the NN model is obtained, an adaptive model predictive control is used to simulate regulation and tracking tasks to test the performance of the NN training and the MPC method.

Keywords:Decentralized control, Distributed control, Networked control systems Abstract: In this paper, we consider cooperative multi-agent systems minimizing a social cost. Each agent tries to minimize the deviation from the average collective behavior and to a local command input. In principle, the optimal solution is centralized and requires a complete communication graph. We study this problem for two important system input-output norms, the l_1 induced norm and the per-agent H2 norm squared, as function of the number of agents, n. For the case of identical agents, we show thatthe optimal social solution is always decentralized and characterize the local optimization problem each agent need to solve. The solution is decentralized but not selfish in the sense that the local optimization problem is not the same as that of a single isolated agent and also depends on the number of agents. In the case of the per-agent H_2 norm squared cost, we have similar results. However, in this case, we show that the optimal decentralized selfish solution is socially optimal in the limit of large n. We study some extensions that include norm constraints, and performance indices not restricted to only penalizing the variations for averages. We also present some extensions of the results to the cases of nonuniform averaging and non uniform agent dynamics. In simple terms, these results, identify important problem classes where decentralized and possibly selfish behaviors are socially optimal, and for which inter-agent communication is unnecessary.

Keywords:Decentralized control, Linear systems, Mechatronics Abstract: The production speed and medium versatility in traditional wide-format printers are limited by medium positioning errors caused by step-wise transportation. The aim of this paper is to develop a repetitive control (RC) framework, that enables continuous media flow printing with enhanced positioning accuracy and increased productivity. The developed framework explicitly addresses the trade-off between performance and model knowledge. Specific solutions that avoid the need for a full multivariable model include i) independent SISO design, and ii) sequential SISO design. The benefits of the pursued RC approach for continuous media flow printing are experimentally validated on an industrial flatbed printer.

Keywords:Decentralized control, Networked control systems, Stochastic systems Abstract: We introduce the concept of randomized information structures for stochastic teams. We further define sub-classes of randomized information structures including randomized static (RS) information structure and randomized partially nested (RPN) information structure that extend their deterministic counterparts. We show that under the LQG setting, optimal decentralized strategies have a switched linear structure under both RS and RPN information structures. Applications in networked control and decentralized networked control are considered.

Keywords:Distributed control, Decentralized control, Computational methods Abstract: The optimal distributed control (ODC) problem for linear discrete-time systems is studied in this paper. The goal is to design a static stabilizing controller that offers some optimality guarantee for the closed-loop system and yet respects an imposed communication structure. Unlike the traditional centralized control problem, the ODC problem is hard to solve in general. Recently, we have introduced an efficient and scalable algorithm to design a distributed controller whose performance is close to that of a given centralized controller, provided that the initial state of the system is known. In this work, we generalize the proposed method to systems with uncertain initial states. The objective is to make the performance of the designed distributed controller be as closely as possible to that of the optimal centralized counterpart for every initial state in the uncertainty region. It is shown that the developed method requires solving a convex problem. Strong theoretical lower bounds are provided on the optimality guarantee of the synthesized distributed controller. To illustrate the effectiveness of the proposed method, case studies on aircraft formation and frequency control of power systems are offered.

Keywords:Distributed control, Decentralized control, Large-scale systems Abstract: In this paper, we address the state estimation problem for multi-agent systems interacting in large scale networks. This research is motivated by the observation that in large-scale networks for many practical applications and domains, each agent only requires information concerning agents spatially close to its location, let’s say topologically k-hop away. We propose a scalable framework where each agent is able to estimate in finitetime the state of its k-hop neighborhood by interacting only with the agents belonging to its 1-hop neighborhood.

Keywords:Agents-based systems, Cooperative control Abstract: We study the problem of distributed maximum computation in an open multi-agent system, where agents can leave and arrive during the execution of the algorithm. The main challenge comes from the possibility that the agent holding the largest value leaves the system, which changes the value to be computed. The algorithms must as a result be endowed with mechanisms allowing to forget outdated information. The focus is on systems in which interactions are pairwise gossips between randomly selected agents. We consider situations where leaving agents can send a last message, and situations where they cannot. For both cases, we provide algorithms able to eventually compute the maximum of the values held by agents.

Keywords:Networked control systems, Control over communications;Information theory and control Abstract: In this paper we develop performance bounds on disturbance power reduction for multiple-input multiple-output networked feedback systems via an information-theoretic approach. The bounds are derived for a system setting with linear time-invariant plants and causal stabilizing controllers communicating over noisy communication channels. We utilize the notion of power gain to measure the worst-case power reduction from the disturbance to the error signal. Concepts from information theory such as entropy and mutual information are instrumental in the analysis, and the performance bounds can be quantified explicitly in terms of the channel blurredness, a measure of poorness on the channel quality, and the plant unstable dynamics.

Keywords:Networked control systems, Control over communications;Switched systems Abstract: The usage of weakly hard real-time constraints to model the loss process in Networked Control Systems aims for studying more realistic network models, while still being able to guarantee stability in the sense of Lyapunov. An open question is, whether it is possible to find a stabilizing controller for a given loss specification, which is the main question of the study at hand. It is formalized as the problem of state feedback stabilizability under weakly hard real-time constraints. To solve the problem, stability conditions for switched systems employing switched Lyapunov functions that have already been shown to be suitable for studying arbitrary bounded packet losses are extended towards the consideration of constrained switching sequences. Those sequences for the weakly hard real-time constraints are represented by a labeled graph that highly relies on an auxiliary system with a restricted, suitably chosen discretization. The overall result shows to be effective in a numerical example.

Keywords:Networked control systems, Decentralized control, LMIs Abstract: We introduce a framework to synthesize block-diagonal H-infinity-controllers with a parametric and a block-triangular dynamic component by convex optimization. This is motivated by a recent H-infinity-design approach for systems with delayed interconnections that are structured according to a strongly-connected communication graph. Our framework allows the extension to synthesis for interconnections described by multiple strongly-connected graphs that are themselves coupled through a nested delay structure.

Keywords:Networked control systems, Decentralized control, Observers for Linear systems Abstract: This letter addresses the consensus problem of multi-agent systems for a static undirected communication topology. It is known that for a static undirected graph, the convergence rate of the consensus protocol depends on the second smallest eigenvalue of the graph Laplacian. The fastest convergence rate can be achieved when the communication topology is given by a complete graph which is costly in terms of the required number of communication links. On the other hand the star topology is ubiquitous in nature and widely used in practical applications due to its robustness property but the convergence rate of the consensus protocol with the star topology is slower than the complete graph. In this letter, we show that the convergence rate of the star topology can be increased by adding observers to each agent except the root agent. The complete graph is chosen as a reference target system and we show that the convergence rate of the consensus protocol with the star topology approaches the convergence rate of the consensus protocol with the complete graph for sufficiently small epsilon, which is a high-gain observer parameter. Furthermore, we show that for sufficiently small epsilon, the trajectories of the agents with the star topology approach the trajectories of the agents with the virtual complete graph. Simulations are provided that show the effectiveness of our theoretical results.

Keywords:Networked control systems;Delay systems, Robust adaptive control Abstract: The distributed control of a network of interconnected systems or agents based on a communication network for exchange of information has attracted considerable interest in research. Delays in the communicated information and the effect of strong interconnections between agents are two of the main challenges a distributed control system has to meet. In this paper we consider a linear Networked Distributed Control System (NDCS) that has strong interactions between its agents, as well as delays in the communication network. We assume that each agent knowns its own state, but receives information about its neighbors’ states with some communication delay τ_{ij}. The control objective is to make each agent track efficiently a reference model by attenuating the effect of the strong interconnections via feedback based on the delayed state information. We prove that if the interconnections can be weakened enough and if the delays are smaller than some bounds which we present, then in the case of known interconnctions the proposed distributed control scheme can guarantee that the tracking error of each agent is bounded in respect to the delays and weakened interconnections, while in the case of unknown interconnections the proposed adaptive distributed control scheme can guarantee that the tracking error of each agent is bounded and small in the mean square sense in respect to the delays and weakened interconnections. Finally, we present an example to demonstrate the applicability and effectiveness of our scheme.

Keywords:Networked control systems, Distributed control, Optimization Abstract: This letter presents a distributed waterfilling algorithm for networked control systems where users communicate with neighbors only. Waterfilling—a well-known optimization approach in communication systems—has inspired practical resolution methods for several control engineering and decision-making problems. This letter proposes a fully distributed solution for waterfilling of networked control systems. We consider multiple coupled waterlevels among users that locally communicate only with neighbors, without a central decision maker.We define two alternative versions (an exact one and an approximated one) of a novel distributed algorithm combining consensus, proximity, and the fixed point mapping theory, and show its convergence. We illustrate the technique by a case study on the charging of a fleet of electric vehicles.

Keywords:Observers for Linear systems, Estimation Abstract: In this paper, we propose a deadbeat state observer for LTI systems having the same dimension of the observed system and with no discontinuous high-gain injection. An invertible time/output dependent coordinate transformation is introduced to convert the original system to a so-named deadbeat observer canonical form, which is endowed with the key property of having known initial conditions. An output-error feedback state observer is then designed for the transformed system, providing deadbeat convergence of the estimates in the ideal case. Remarkably, in presence of noisy input and output signals, the estimation error is proved to be ISS with respect to the measurement noise and its bound is characterized theoretically. Numerical simulations are carried out to examine the effectiveness of the proposed observer in comparison with an existing finite-time method.

Keywords:Linear systems, Adaptive control, Optimization Abstract: A multiple model switched repetitive control (RC) framework is developed for a general class of system and widely used RC update structure. This guarantees stability and robust performance under the assumption that the true plant model belongs to a plant uncertainty set specified by the designer. A comprehensive design procedure for the candidate model set and RC update is presented based on novel application of gap metric analysis to RC, and switching of the corresponding RC schemes is achieved efficiently using a bank of Kalman filters.

Keywords:Linear systems, Model/Controller reduction Abstract: The model reduction problem for continuous-time, linear, time-invariant systems is studied at isolated singularities of the transfer function. The moments at a pole of the transfer function are shown to be uniquely specified by the solutions of certain Sylvester equations exploiting two distinct approaches based on complex analysis and on geometric control theory. This allows to determine reduced order models which preserve given poles and match the corresponding moments. An in-depth analysis of the assumptions underlying this approach is provided in a companion paper. The applicability of the approaches developed is demonstrated with simple academic examples.

Keywords:Linear systems, Network analysis and control, Closed-loop identification Abstract: We consider networks of linear, time-invariant systems defined over matrices of rational functions in a complex variable where each element of the matrix represents a link in the network. When these rational functions are proper, but not necessarily strictly proper, we demonstrate the necessary and sufficient conditions under which such a network configuration is well-posed. We include multiple examples of network configurations and their respective well-posedness conditions, including cases where two or more ill-posed network configurations can be interconnected to form a well-posed network.

Keywords:Linear systems, Optimization, Optimization algorithms Abstract: Full-state feedback control requires complete state information to compute the desired controller. In many applications either complete information about the state is not available or not required to control the system. In those cases, static output feedback (SOF) controller can be used as an alternative framework for feedback control. Structured SOF (SSOF) is a class of SOF for which the controller gain structure is predefined. In this work, a new output feedback gain design procedure is proposed for a class of linear time invariant systems controlled by SSOF controllers. The SSOF synthesis problem is posed as an optimization problem with a Lyapunov equation like constraint which is quadratic in the gain variables. The problem is reformulated as an optimization problem with a Bilinear Matrix Inequality constraint. An iterative combination of generalized Benders decomposition and gradient projection method is used to solve the proposed design problem. Necessary and sufficient conditions are derived to test the minimality of the computed solution. Finally, the proposed formulation is demonstrated through an engineering example.

Keywords:Linear systems, Stability of linear systems Abstract: A passivating control architecture for SISO linear systems, comprising dynamic feedback and feedforward, is proposed in this paper. The approach - essentially without any restriction and by relying only on input/output measurements - provides a closed-loop system that is passive from a new control input to a modified output. The result is achieved by arbitrarily assigning the relative degree and the location of poles and zeros on the complex plane of the interconnected system in a systematic way. It is also shown that similar ideas can be employed to enforce a desired, arbitrarily small, L2-gain from an unknown disturbance input to a modified output, while preserving the corresponding gain from the control input to the same output. The paper is concluded with applications and further discussions on the results, which include constructions towards the adaptive control of non-minimum phase systems.

Keywords:Discrete event systems;Petri nets, Optimal control Abstract: The topic of this paper is modeling and control of timed discrete event systems in a dioid framework if systems operate under the restriction of shared resources. The behavior of such systems can be elegantly modeled using the Hadamard product of series in dioids. Using residuation of the Hadamard product, it is possible to compute optimal control, where optimality is in the sense of a lexicographical order reflecting the chosen prioritization of subsystems. The paper concludes with an example, illustrating the efficiency of the proposed method.

Keywords:Discrete event systems;Petri nets, Optimal control Abstract: Timed Event Graphs (TEGs) and their weighted extension WTEGs are particular timed Discrete Event Systems (DESs) where the dynamic behavior is described by synchronization and saturation effects. With dioids, a linear systems theory has been developed for (weighted) TEGs. In this paper, we use dioid theory to model the input-output behavior of a WTEG. Furthermore, we propose a control strategy which determines an optimal input for a predefined reference output. In this case, ”optimal” means that input events are scheduled as late as possible with the restriction that the output events of the system do not occur later than specified by the reference. This strategy is often referred to as ”just-in-time” control in literature.

Keywords:Discrete event systems;Petri nets;Supervisory control Abstract: In this paper we aim to characterize the admissible marking set in partially controllable Petri nets with weighted arcs. We consider a special subclass of generalized Petri nets called weighted state machines (WSMs) that have state-machine topology with weighted arcs. Such subclass of nets can be used to model systems of practical interest characterized by operations executed in batches. We propose a dynamic programming method to compute the admissible marking set of an elementary GMEC for a net in this subclass. Such a result can be used to design an online control logic to ensure that the current marking is always covered by some maximal admissible markings and is always admissible.

Keywords:Discrete event systems;Supervisory control;Automata Abstract: In order to operate discrete event systems, agents utilize information from observing event sequences. This information is often incomplete due to communication defects, sensor malfunction, etc. Thus, understanding partial observation is essential to operating these systems. We develop, in this paper, an algorithm that reduces relative observability problems to observability problems in the context of centralized systems. Our extended results are in the context of a general language-based dynamic observation setup in which event-based observation in literature is a special case.

Keywords:Discrete event systems;Supervisory control;Automata Abstract: In this paper, we study the supervisory control problem of Timed Networked Discrete Event Systems (TNDES) subject to delays and losses of observations. We assume that the plant is distributed, having, therefore, several communication channels for the transmission of the event observations to the supervisor. We propose a timed model based on, a priori, knowledge of the minimal transition activation time and on the maximal time delay (possible packet loss also) of the communication channels. We then, obtain an equivalent untimed one and, based on this model we formulate a supervisory control problem (here referred to as networked supervisory control), and present a necessary and sufficient existence condition and a method for the synthesis of networked supervisors.

Keywords:Discrete event systems Abstract: This paper extends prior work about the enforcement of opacity by insertion functions and applies a more general method that uses edit functions. Based on its observations, the edit function can insert or erase events to modify the outputs of the system and obfuscate the outside intruder. In this paper, a key assumption is that the intruder knows the implementation of the edit function, which requires the edit function to be public-private enforcing. In order to capture the limitations of edit functions, state based edit constraints are introduced and may preclude some originally feasible edit choices, complicating the enforcement problem. The edit function in this work is deterministic and the enforcement problem is formulated as a two-player game between the edit function and the system. Our goal is to synthesize public-private enforcing edit functions without violating edit constraints. A new synthesis algorithm is proposed based on the game structure.

Keywords:Lyapunov methods, Nonlinear output feedback, Robust control Abstract: A robust nonlinear output feedback control method is presented, which achieves three degree of freedom (3-DOF) attitude control of a hover system test bed. The proposed control method formally incorporates the practical limitations in the voltage control inputs to the control actuators (i.e., the quadrotor propellers). In addition, the control law is designed to compensate for uncertainty in the hover system dynamic model, including input-multiplicative parametric uncertainty resulting from uncertain drag and friction coefficients in the propellers’ dynamic model. To reduce the computational requirement in the closed-loop system, constant feedforward estimates of the input-multiplicative uncertainty are utilized in lieu of adaptive parameter estimates. Eschewing the high-gain feedback requirement that is characteristic of standard sliding mode observer methods, the proposed control method utilizes a bank of dynamic filters, which operates as a velocity estimator in the closed-loop system. A rigorous error system development and Lyapunov-based stability analysis are presented to prove that the proposed output feedback control law achieves asymptotic 3-DOF attitude control in the presence of parametric input uncertainty and unmodeled dynamics. Experimental results are provided to demonstrate the performance of the attitude control method using the Quanser 3-DOF hover system test bed.

Keywords:Lyapunov methods, Differential-algebraic systems, Control of metal processing Abstract: Motivated by a real problem in cold rolling of steel, we propose a design methodology of guaranteed cost controllers for descriptor type systems in presence of multiple time-varying delays. We first analyse the existence and uniqueness of solutions for the class of systems under study. This enable us to define the compatible initial conditions for this class of systems and show that for the closed-loop dynamics they depend on the controller. Consequently, we provide a methodology to avoid this dependency. Secondly, we consider the problem of designing a controller that stabilizes the system and ensures some performance guarantees. The proposed solution consists of minimizing a cost function related to the energetic aspects of the system. The main tool used for the control design is a modified Lyapunov--Krasovskii functional that takes into account the singularity of the system. Our solution can be easily implemented since the controller is obtained by solving some linear matrix inequalities (LMIs). A numerical example illustrates the implementation of our results.

Keywords:Lyapunov methods, Stability of nonlinear systems, Computational methods Abstract: This paper proves the existence of polynomial Lyapunov functions for rationally stable vector fields. For practical purposes the existence of polynomial Lyapunov functions plays a significant role since polynomial Lyapunov functions can be found algorithmically. The paper extents an existing result on exponentially stable vector fields to the case of rational stability. For asymptotically stable vector fields a known counter example is investigated to exhibit the mechanisms responsible for the inability to extend the result further.

Keywords:Lyapunov methods, Stability of nonlinear systems;Nonholonomic systems Abstract: Theoretical results for the existence of (nonsmooth) control Lyapunov functions (CLFs) for nonlinear systems asymptotically controllable to the origin or a closed set have been available since the late 1990s. Additionally, robust feedback stabilizers based on such CLFs have also been available though, to the best of our knowledge, these stabilizers have not been implemented. Here, we numerically investigate the properties of the closed loop solutions of the nonholonomic integrator using three control techniques based on the knowledge of two different nonsmooth CLFs. In order to make the paper self-contained, we review theoretical results on the existence of nonsmooth CLFs.

Keywords:Lyapunov methods, Stability of nonlinear systems Abstract: The problem of synthesis of a homogeneous Lyapunov function for an asymptotically stable homogeneous system is studied. For systems with a nonnegative degree of homogeneity, a numeric procedure is proposed, which provides a digital representation of a homogeneous Lyapunov function. The results are illustrated by two planar examples of linear and nonlinear systems.

Keywords:Lyapunov methods;Vision-based control;Switched systems Abstract: Conventional methods for image-based guidance, navigation, and control of a wheeled mobile robot (WMR) require continuous, uninterrupted state feedback at all times. However, tracked features may be lost due to occlusions or the trajectory of the WMR. In this paper, a set of dwell time conditions that can be used for trajectory design have been developed to relax the constant visibility constraint, while maintaining the ability to self-localize and track a desired trajectory. The use of a predictor for state estimates when landmark features are not visible helps to extend the time before image feedback of landmark features is required. Using Lyapunov-based switched systems analysis methods, maximum and minimum dwell time conditions are derived for periods when features are visible or not. A simulation is performed with a trajectory formed by Bézier splines to demonstrate a globally uniformly ultimately bounded trajectory tracking result despite intermittent measurements.