In a paper by Willems et al. , it was shown that persistently exciting data can be used to represent the input–output behavior of a linear system. Based on this fundamental result, we derive a parametrization of linear feedback systems that paves the way to solve important control problems using data-dependent linear matrix inequalities only. The result is remarkable in that no explicit system's matrices

We study event-triggered control for stabilization of unstable linear plants over rate-limited communication channels subject to unknown bounded delay. On one hand, the timing of event triggering carries implicit information about the state of the plant. On the other hand, the delay in the communication channel causes information loss, as it makes the state information available at the controller out

In 1970, Binyamin Schwarz defined and analyzed totally positive differential systems (TPDSs), i.e., linear time-varying systems whose transition matrix is totally positive. He showed that any solution of a TPDS satisfies a sign variation diminishing property with respect to the standard number of sign variations. It has been recently shown that several important results on entrainment [stability] in

This paper investigates an adaptive sliding-mode controller design problem for a class of Markov jump systems with actuator faults. First, a more extensive faults model, which includes actuator loss of effectiveness, outage and stuck, is established. Moreover, a novel sliding surface function is designed and sufficient conditions are established to ensure stochastic stability of the Markov jump systems

In this paper, we study connections between structured storage or Lyapunov functions of a class of interconnected systems (dynamical networks) and dissipativity properties of the individual systems. We prove that if a dynamical network, composed as a set of linear time invariant systems interconnected over an acyclic graph, admits an additive quadratic Lyapunov function, then the individual systems

This paper considers an unconstrained collaborative optimization of a sum of convex functions, where agents make decisions using local information in the presence of random interconnection topologies. We recast the problem as minimization of the sum of convex functions over a constraint set defined as the set of fixed-value points of a random operator derived from weighted matrices of random graphs

Fluid models are a popular formalism in the quantitative modeling of biochemical systems and analytical performance models. The main idea is to approximate a large-scale Markov chain by a compact set of ordinary differential equations (ODEs). Even though it is often crucial for a fluid model under study to satisfy some given properties, a formal verification is usually challenging. This is because

This paper considers the problem of learning the unknown structure of a network with the underlying topology given by a polyforest (a collection of directed trees with potentially multiple roots). The main result is an algorithm that consistently learns the network structure using only second-order statistics of the data. The methodology is robust with respect to the presence of unmeasured (latent)

This paper considers the problem of efficiently and securely controlling cyber-physical systems that are operating in uncertain, and adversarial environments. To mitigate sensor, actuator attacks, and performance loss due to such attacks, we formulate a secure control algorithm that consists of a proactive and a reactive defense mechanism. The proactive mechanism, which is based on the principles of

We propose a distributed model predictive control scheme for linear time-invariant constrained systems that admit a separable structure. To exploit the merits of distributed computation algorithms, the terminal cost and invariant terminal set of the optimal control problem need to respect the coupling structure of the system. Existing methods to address this issue typically separate the synthesis of

We consider a class of continuous-time hybrid dynamical systems that correspond to subgradient flows of a piecewise linear and convex potential function with finitely many pieces, and which includes the fluid-level dynamics of the Max-Weight scheduling policy as a special case. We study the effect of an external disturbance/perturbation on the state trajectory, and establish that the magnitude of this

In this paper, a feedback controller designed to realize reachability is investigated in a class of nonlinear systems. Different from stability, reachability defines that there exists an input such that a closed-loop system can reach the desired states from any other initial states within finite time. In other words, the state errors can converge to zero in finite time if reachability can be realized

This paper concerns a spectral estimation problem in which we want to find a spectral density function that is consistent with estimated second-order statistics. It is an inverse problem admitting multiple solutions, and selection of a solution can be based on prior functions. We show that the problem is well-posed when formulated in a parametric fashion, and that the solution parameter depends continuously

In this paper, we analyze the behavior of stochastic approximation schemes with set-valued maps in the absence of a stability guarantee. We prove that after a large number of iterations, if the stochastic approximation process enters the domain of attraction of an attracting set, it gets locked into the attracting set with high probability. We demonstrate that the above-mentioned result is an effective

In this paper, we develop a robust economic model predictive controller for the containment of stochastic susceptible-exposed-infected-vigilant (SEIV) epidemic processes, which drives the process to extinction quickly, while minimizing the rate at which control resources are used. The study we present here is significant in that it addresses the problem of efficiently controlling general stochastic

This paper proposes scalable, distributed algorithms for solving linear equations by integrating two mechanisms, termed consensus and conservation, in double-layered multiagent networks. The multiagent network considered in this paper is composed of clusters and each cluster consists of an aggregator and a subnetwork of agents. By achieving consensus and conservation through agent–agent communications

This paper investigates the problem of minimizing the global energy cost for multiagent systems to achieve consensus over undirected network topologies. Conventional optimization problems so defined require all-to-all network topologies for interagent communications, which precludes the use of distributed control or otherwise demands that the network be complete. To circumvent this difficulty, we introduce

In this paper, we study robust stabilization for the networked control system (NCS) over the communication channels described by cascaded two-port networks. Such an NCS involves simultaneous uncertainties in the plant, controller, and two-port communication channels. The cascaded two-port connections arise when signals in the NCS are transmitted through bidirectional communication channels separated

We study the time optimal control problem for the evolution operator of an $n$ -level quantum system. For the considered models, the control couples all the energy levels to a given one and is assumed to be bounded in Euclidean norm. The resulting problem is a sub-Riemannian $K\hbox{--}P$ problem, (as introduced in articles by U. Boscain and by V. Jurdjevic), whose underlying symmetric space is $SU(n)/S(U(n-1)

The goal of this note is to highlight that the reformulation of saturation as constraints, typical of the computation of the maximal output admissible sets used in the reference governor literature, is not without loss of generality and may introduce a relevant amount of conservativeness. The note also proposes a modified reference governor able to overcome this limitation.

This note presents a new unknown input observer (UIO) with adjustable performances for linear systems. It focuses on state estimation in the presence of zeros with slow dynamics. It is known that if the system has stable invariant zeros and satisfies the decoupling condition then a UIO exists. However, the invariant zeros constrain the pole placement and consequently the state estimation error convergence

We investigate integral input-to-state stability (iISS) of nonlinear networked control systems (NCSs). The controller is designed by emulation, i.e., it is constructed to ensure iISS for the closed-loop system in the absence of the network. Afterward, the latter is taken into account and explicit conditions are provided on the scheduling protocol and the maximum allowable transmission interval to preserve

This paper considers the quadratic control problem of discrete-time Markov jump linear systems with constraints on the norm of the state and control variables. We assume that the Markov chain parameter is not available, and instead, there is a detector, which emits signals providing information on this parameter. It is desired to derive a feedback linear control using the information provided by this

This note is concerned with a suboptimal version of the distributed linear quadratic optimal control problem for multiagent systems. Given a multiagent system with identical agent dynamics and an associated global quadratic cost functional, our objective is to design distributed control laws that achieve consensus and whose cost is smaller than an a priori given upper bound, for all initial states

This paper presents an optimal data fusion formulation and algorithm in the sense of minimum mean-square error when some or all cross covariances are unknown. This algorithm is generic in that it is capable of processing any number of measurement vectors of any dimension with any pattern of unknown cross covariances. Closed-form solution is provided for the case when all cross covariances are unknown

Opacity, a confidentiality property, is an increasing concern in cyber-physical systems (CPSs) that are vulnerable to an intruder which intends to reveal a “secret” of a system. This note presents a new framework for opacity and considers the problem of enforcing opacity in CPSs modeled as linear time-invariant systems. The confidential information involves the CPS’ interference attenuation capacity

In this paper, a numerically robust solver for least-square problems with bounded variables (BVLS) is presented for applications including, but not limited to, model predictive control (MPC). The proposed BVLS algorithm solves the problem efficiently by employing a recursive QR factorization method based on Gram–Schmidt orthogonalization. A reorthogonalization procedure that iteratively refines the

This note establishes the exponential input-to-state stability (EISS) property for a clamped-free damped string with respect to distributed and boundary disturbances. While efficient methods for establishing ISS properties for distributed parameter systems with respect to distributed disturbances have been developed during the last decades, establishing ISS properties with respect to boundary disturbances

This work addresses the problem of inverse reinforcement learning in Markov decision processes where the decision-making agent is risk-sensitive . In particular, a risk-sensitive reinforcement learning algorithm with convergence guarantees that makes use of coherent risk metrics and models of human decision-making which have their origins in behavioral psychology and economics is presented. The risk-sensitive

This paper addresses a finite-time sliding-mode control problem for a class of Markovian jump cyber-physical systems. It is assumed that the control input signals transmitted via a communication network are vulnerable to cyber-attacks, in which the adversaries may inject false data in a probabilistic way into the control signals. Meanwhile, there may exist randomly occurring uncertainties and peak-bounded

The self-learning optimal regulation for discrete-time nonlinear systems under event-driven formulation is investigated. An event-based adaptive critic algorithm is developed with convergence discussion of the iterative process. The input-to-state stability (ISS) analysis for the present nonlinear plant is established. Then, a suitable triggering condition is proved to ensure the ISS of the controlled

This paper is devoted to the characterization of existence and uniqueness conditions for the stabilizing solution of a large class of Riccati equations arising in stochastic dynamic games. As an application of the obtained results, we consider in a second step the problem of a zero-sum two players linear quadratic difference game for a stochastic discrete-time dynamical system subject to both random

This paper proposes an iterative learning control (ILC) algorithm with gain adaptation for discrete-time stochastic systems. The algorithm is based on Kesten's accelerated stochastic approximation (SA) algorithm. The gain adaptation uses only tracking error information, and, hence, is a data-driven adaptation approach. If stochastic noises account for a small proportion of the tracking error, the learning

In this paper, the unscented Kalman filtering (UKF) problem is investigated for a class of general nonlinear systems with stochastic uncertainties under communication protocols. A modified unscented transformation is put forward to account for stochastic uncertainties caused by modeling errors. For preventing data collisions and mitigating communication burden, the round-robin protocol and the weighted

In this paper, we study the problem of achieving average consensus over a random time-varying sequence of directed graphs by extending the class of so-called push-sum algorithms to such random scenarios. Provided that an ergodicity notion, which we term the directed infinite flow property, holds and the auxiliary states of agents are uniformly bounded away from zero infinitely often, we prove the almost

This paper presents an improved backstepping control implementation scheme for a $n$ -dimensional strict-feedback uncertain nonlinear system based on command filtered backstepping and adaptive neural network backstepping. In this approach, $n$ command filters and one neural network are applied to reconstruct the approximations of unknown nonlinearities, which are related to the system uncertainties

In this paper, the issue of stochastic stabilization of nonlinear systems via intermittent Brownian motion under random disturbance is investigated. Two types of noise are considered. One is Brownian noise that is regarded as an intermittent control input. The other is the random disturbance caused by the unknown external environment, which can lead to time fluctuations of the intermittent Brownian

This paper addresses model-set-based quantitative analysis of feedback systems. In particular, we find a model set describing the subsystems such that the performance improvement of the feedback system is achieved. To this end, we introduce the parameter-integrated passivity to accurately describe each passive subsystem and their feedback system. A model set describing passive systems is characterized

In this note, constant reference tracking using integral control for negative-imaginary (NI) systems preceded by hysteresis nonlinearity is addressed. The hysteresis nonlinearity is considered to be slope-restricted, though no specific modeling framework is exploited. It is effectively shown that integral tracking controllers are guaranteed to exist when a stable NI system with sign-definite dc-gain

This paper focuses on the problem of global finite-time stabilization for a class of uncertain nonlinear systems with event-triggered inputs. The existing event-based design methods can only partially compensate for the effects of the event-triggered errors and cannot completely counteract them to achieve finite-time control. For this reason, a new method about event triggering mechanism and event-triggered

This paper proposes a global sliding mode observer for a class of nonlinear mechanical systems with two degrees of freedom. For the observer design, besides the usual Coriolis and centrifugal forces, we consider (discontinuous) dry and viscous friction and non vanishing uncertainties/perturbations. Moreover, the system is not required to be bounded-input-bounded-state, rendering the observer design

We present a new approach for the stability analysis of linear coupled differential-difference systems with a general distributed delay. The distributed delay term in this note can contain any ${\mathbb{L}}^{2}$ function which is approximated via a class of elementary functions including polynomial, trigonometric, exponential functions, etc. Through the application of a new proposed integral inequality

Presents the table of contents for this issue of the publication.

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.

General-purpose correct-by-construction synthesis methods are limited to systems with low dimensionality or simple specifications. In this paper, we consider highly symmetrical counting problems and exploit the symmetry to synthesize provably correct controllers for systems with tens of thousands of states. The key ingredients of the solution are an aggregate abstraction procedure for mildly heterogeneous

This paper proposes an efficient marking estimation method for a subclass of time labeled Petri nets (TLPNs), in which each transition is associated with an infinite upper bound delay. The unobservable subnet of the considered subclass of TLPNs is backward-conflict-free, and all the output transitions of each conflict place are observable. The highlight of this method is that the markings set consistent

One often wishes for the ability to formally analyze large-scale systems-typically, however, one can either formally analyze a rather small system or informally analyze a large-scale system. This paper tries to further close this performance gap for reachability analysis of linear systems. Reachability analysis can capture the whole set of possible solutions of a dynamic system and is thus used to

A constructive tool of nonlinear control system design, the method of control Lyapunov functions (CLFs), has found numerous applications in stabilization problems for continuous-time, discrete-time, and hybrid systems. In this paper, we address the fundamental question: Given a CLF, corresponding to a continuous-time controller with some predefined (e.g., exponential) convergence rate, can the same

We consider a certain class of nonlinear maps that preserve the probability simplex, i.e., stochastic maps, which are inspired by the DeGroot-Friedkin model of belief/opinion propagation over influence networks. The corresponding dynamical models describe the evolution of the probability distribution of interacting species. Such models where the probability transition mechanism depends nonlinearly

We study stochastic team problems with static information structure where we assume controllers have linear information and quadratic cost but allow the noise to be from a non-Gaussian class. When the noise is Gaussian, it is well known that these problems admit linear optimal controllers. We show that for such linear-quadratic static teams with any log-concave noise, if the length of the noise or

This paper presents new sufficient conditions for convergence and asymptotic or exponential stability of a stochastic discrete-time system, under which the constructed Lyapunov function always decreases in expectation along the system's solutions after a finite number of steps, but without necessarily strict decrease at every step, in contrast to the classical stochastic Lyapunov theory. As the first

We propose control and communication strategies for nonlinear networked control systems subject to state and input constraints. The objective is to steer the state of the system toward a prescribed target set in finite time ( reachability ), while at the same time remaining inside a safety set for all time ( safety ). By leveraging the notion of the $\delta$ -input-to-state stability (ISS) control

In this paper, we propose two iterative algorithms to identify transfer functions of two-dimensional (2-D) systems. The proposed algorithms are modifications of the 2-D adaptive Fourier decomposition (AFD) and weak pre-orthogonal adaptive Fourier decomposition (W-POAFD). 2-D AFD and W-POAFD are newly established adaptive representation theories for multivariate functions utilizing, respectively, the

In this paper, we consider dynamic channel allocation for remote state estimation of multiagent systems. For each subsystem, a sensor measures its state and transmits the data via a packet-dropping channel, which is dynamically allocated by the remote estimator. We first formulate the problem as a Markov decision process. Given the difficulty of obtaining an optimal policy of large-scale problems,

We consider an event-triggered controller synthesis problem to replace the continuous feedback policy with an intermittent feedback policy for a nondeterministic linear system. An event-triggered framework communicates the measurement to the controller only at certain discrete time instances which are generated by an event generator. The objective of this paper is to synthesize an optimal-event generator

Periodic event-triggered control (PETC) is an appealing paradigm for the implementation of controllers on platforms with limited communication resources, a typical example being networked control systems. In PETC, transmissions over the communication channel are triggered by an event generator, which depends solely on the available plant and controller data and is only evaluated at given sampling instants

Reinforcement learning (RL) is a powerful tool to perform data-driven optimal control without relying on a model of the system. However, RL struggles to provide hard guarantees on the behavior of the resulting control scheme. In contrast, nonlinear model predictive control (NMPC) and economic NMPC (ENMPC) are standard tools for the closed-loop optimal control of complex systems with constraints and

A large-scale complex system comprising many, often spatially distributed, dynamical subsystems with partial autonomy and complex interactions are called system of systems. This paper describes an efficient algorithm for model predictive control of a class of system of systems for which the overall objective function is the sum of convex quadratic cost functions of (locally) constrained linear subsystems

In this paper, a novel distributed stochastic approximation algorithm (DSAA) is proposed to seek roots of the sum of local functions, each of which is associated with an agent from multiple agents connected over a network. At each iteration, each agent updates its estimate for the root utilizing the noisy observations of its local function and the information derived from the neighboring agents. The

As autonomous vehicle technology advances rapidly, the design and operation of networks composed of fully autonomous vehicles have attracted immense interest. It is widely anticipated that fully autonomous vehicle networks will drastically improve performance. In this paper, we consider a widely studied problem, in which autonomous vehicles arriving at an intersection adjust their speeds to traverse