This paper investigates the containment control problem for multi-agent systems (MASs) with multiple leaders which have non-zero control inputs. Agents’ tracking errors are described by a linear system with an external disturbance. The containment control problem is then solved by applying a distributed observer-based output feedback controller. Anti-windup (AW) compensation is designed by convex conditions

This paper aims to design model-free fractional order differentiators to non-asymptotically and robustly estimate both the Riemann–Liouville and Caputo fractional derivatives of an unknown signal from its discrete and noisy observation. To achieve this, new fractional integration by parts formulas are first introduced. Then, by applying the generalized modulating functions method to a constructed model

The problem of cooperative protection in the presence of a superior opponent is considered. The note addresses the Target–Attacker–Defender problem. The challenging case where the Attacker is faster than the Target and the Defender and also more lethal than the Defender is considered. This note presents novel cooperative strategies for the Target and Defender where they need to rendezvous and to coordinate

Reinforcement learning (RL) is promising for complicated stochastic nonlinear control problems. Without using a mathematical model, an optimal controller can be learned from data evaluated by certain performance criteria through trial-and-error. However, the data-based learning approach is notorious for not guaranteeing stability, which is the most fundamental property for any control system. In this

This paper investigates the remote state estimation for a cyber–physical system (CPS) where a group of (primary) sensors transmit sensing data packets to the remote estimators for state estimation via their individual channels. In view of the complexity arising from the scale of such system, it is desirable for the primary sensors to share their channels with the newly-added (potential) ones, especially

A basic simulation-based reinforcement learning algorithm is the Monte Carlo Exploring Starts (MCES) method, also known as optimistic policy iteration, in which the value function is approximated by simulated returns and a greedy policy is selected at each iteration. The convergence of this algorithm in the general setting has been an open question. In this paper, we investigate the convergence of

In this paper, to eliminate the tracking error by using adaptive dynamic programming (ADP) algorithms, a novel formulation of the value function is presented for the optimal tracking problem (TP) of nonlinear discrete-time systems. Unlike existing ADP methods, this formulation introduces the control input into the tracking error, and ignores the quadratic form of the control input directly, which makes

We consider a failure diagnosis problem for discrete event systems in the presence of sensor failures. In most existing works on failure diagnosis subject to sensor failures, intermittent sensor failures and permanent ones are dealt with separately. The case where any event in a subset of the observable event set possibly becomes unobservable intermittently or permanently can be modeled in the existing

Opinion dynamics expressed by the bounded confidence discrete-time heterogeneous Hegselmann–Krause model is considered. A policy for the adaptation of the agents confidence thresholds based on heterophily, maximum number of neighbors and non-influencing similarity interval is proposed. The policy leads to the introduction of the concepts of practical clustering and practical consensus. Several properties

Volterra series approximate a broad range of nonlinear systems. Their identification is challenging due to the curse of dimensionality: the number of model parameters grows exponentially with the complexity of the input–output response. This fact limits the applicability of such models and has stimulated recently much research on regularized solutions. Along this line, we propose two new strategies

This paper is aimed at cooperative iterative learning tasks for leaderless networks with nonlinear agents, and the effects arising from switching topologies, locally Lipschitz nonlinearities, initial state shifts, and external disturbances of agents are addressed. By proposing a learning-based distributed algorithm, desired relative formation behaviors of leaderless networks can be realized, where

We study platoon control of vehicles with linear third-order longitudinal dynamics under the constant time headway policy. The controller of each follower vehicle is only based on its own velocity, acceleration, inter-vehicle distance and velocity difference with respect to its immediate predecessor, which are all obtained by on-board sensors. We develop distributed cooperative extended state observers

While the estimation problem of the unknown frequencies for a continuous-time multi-tone sinusoidal signal was thoroughly solved in the early 2000s, the discrete-time version of this problem was studied only recently via a nonlinear control approach. In this paper, we present a simpler approach to the estimation problem of the unknown frequencies for a discrete-time multi-tone sinusoidal signal. Our

In this paper, the distributed set-membership filtering problem is studied for a class of discrete time-varying systems over digital wireless sensor networks. In order to make full utilization of limited bandwidth of the communication channels connecting sensor nodes, a new coding–decoding communication strategy is implemented to regulate the data transmission between individual nodes. Under the scheduling

In this paper, we extend unconstrained model predictive control (MPC) from setpoint stabilization to dynamic reference tracking for continuous-time nonlinear systems. In particular, we focus on the case when the reference cannot be perfectly tracked by the system due to dynamics and/or constraints. Under the incremental stabilizability assumption and an additional dissipativity assumption, the practical

In this paper, we investigate a general nonlinear model of opinion dynamics in which both state-dependent susceptibility to persuasion and antagonistic interactions are considered. According to the existing literature and socio-psychological theories, we examine three specializations of state-dependent susceptibility, that is, stubborn positives scenario, stubborn neutrals scenario, and stubborn extremists

In this technical communique, a locally convergent continuous-time model for the Durand–Kerner method is proposed to simultaneously determine all the roots of a time-varying polynomial. The effectiveness of this method is demonstrated through its application to a benchmark example.

In this paper, we study a class of decentralized online convex optimization problems with time-varying loss functions over multi-agent networks. We propose a decentralized online subgradient method by using only the signs of the relative states of neighbors, which considerably reduces the sensing and communication requirements for the agents. We show that, despite the loss of information, the proposed

Deployment of the first-order and second-order nonlinear multi agent systems over desired open (and, as a particular case, closed) smooth curves in 2D or 3D space is considered. The considered nonlinearities are globally Lipschitz. We assume that the agents have access to the local information of the desired curve and to their positions with respect to their closest neighbors (as well as to their velocities

We propose a methodology for the identification of nonlinear state–space models from input/output data using machine-learning techniques based on autoencoders and neural networks. Our framework simultaneously identifies the nonlinear output and state-update maps of the model. After formulating the approach and providing guidelines for tuning the related hyper-parameters (including the model order)

This paper proposes two distributed state estimation protocols for static linear cyber–physical systems under Byzantine links/nodes caused by adversarial attacks. First, a basic version of secure distributed algorithm is proposed where the influence of the Byzantine links/nodes is countered by adopting a local min-switching decision (LMSD) rather than the existing coordinate-wise trimmed means (CWTM)

In this paper a novel distributed adaptive dual-layer super-twisting sliding mode observer-based scheme is designed to isolate, reconstruct and mitigate the effects of disturbances and a class of communication attacks affecting both generator nodes and load nodes in power networks. Voltage phase angles are measured at each node by means of Phasor Measurement Units (PMUs). Based on this information

In this paper we address the problem of state observation of linear time-varying (LTV) systems with delayed measurements, which has attracted the attention of many researchers—see Sanz et al. (2019) and references therein. We show that, the parameter estimation-based observer (PEBO) design proposed in Ortega et al., 2021, Ortega et al., 2015 provides a very simple solution to the problem with reduced

We consider strongly monotone games with convex separable coupling constraints, played by dynamical agents, in a partial-decision information scenario. We start by designing continuous-time fully distributed feedback controllers, based on consensus and primal–dual gradient dynamics, to seek a generalized Nash equilibrium in networks of single-integrator agents. Our first solution adopts a fixed gain

Partially observable Markov decision process (POMDP) is a comprehensive modeling framework that captures uncertainties from sensing noises, actuation errors, and environments. Traditional POMDP planning finds an optimal policy for reward maximization. However, for safety-critical applications, it is often necessary to guarantee system performance described by high-level temporal logic specifications

Most existing literature about the discrete-time Cucker−Smale model focus on the asymptotic flocking behavior. When the communication weight has a long range, asymptotic flocking holds for any initial data. Actually, the velocity of every agent will exponentially converge to the same limit in this case. However, when the communication weight has a short range, asymptotic flocking does not exist for

This paper proposes a robust tube-based model predictive control for polytopic linear parameter varying systems subject to bounded additive disturbances. In the proposed method, the future scheduling evolutions are described by a known nominal trajectory and some constant uncertainty sets around the nominal signals. Then, by using the notion of polar dual set, a cross-section varying tube parameterization

This paper considers the design of fully distributed Nash equilibrium seeking strategies for networked games. To develop fully distributed seeking strategies, two adaptive control laws, including a node-based control law and an edge-based control law, are proposed. In the node-based adaptive strategy, each player adjusts their own weight on their procurable consensus error dynamically. Moreover, in

We propose a new design for a repetitive control scheme for nonlinear minimum-phase systems with arbitrary relative degree and globally Lipschitz nonlinearities. We represent the delay of the repetitive control scheme as a transport equation and we propose a new forwarding-based (partial) state-feedback design that uses not only the boundary information of the delay, but the entire state of the transport

This paper develops a distributed approach for inverse optimal control (IOC) in multi-agent systems. Here each agent can only communicate with certain nearby neighbors and only accesses segments of system’s trajectory, which is not sufficient for the agent to solve the IOC problem alone. By introducing the concept of the data effectiveness and bridging the connection between each segment and its contribution

This paper presents new methods for set-valued state estimation of discrete-time nonlinear systems whose trajectories are known to satisfy nonlinear equality constraints, called invariants (e.g., conservation laws). Set-valued estimation aims to compute tight enclosures of the possible system states in each time step subject to unknown-but-bounded uncertainties. Most existing methods employ a standard

In this paper, we consider absolute stability for an axially moving Kirchhoff beam under nonlinear boundary feedback controls. The nonlinear boundary control which satisfies a slope-restricted condition is a negative feedback of the transverse velocity at the right eyelet of the moving beam. First, the well-posedness of the resulting closed-loop system is established by means of the Faedo–Galerkin

This paper presents a novel reduced-order algorithm for identifying rational models. From the Arnoldi process, an orthonormal basis of the Krylov subspace is constructed. Based on the Krylov subspace, a high-order cost function is transformed into a low-order one, thereby significantly reducing the computational efforts. This algorithm can be considered as a reduced-order least squares (LS) algorithm

For the constrained multi-armed bandit model, we show that by construction there exists an index-based deterministic “convergent optimal” algorithm. The optimality is achieved by the convergence of the probability of choosing an optimal feasible arm to one over infinite horizon. The algorithm is built upon Locatelli et al. (2016) “anytime parameter-free thresholding” algorithm under the assumption

In this paper, neuroadaptive fault-tolerant control of nonlinear vehicular platoon systems subject to unknown direction actuator faults, unmodeled dynamics, and external disturbances is investigated. A vehicle model considering the influence of velocity direction deflection angle on vehicle position is proposed on a two-dimensional (2-D) plane to realize multi-lane vehicle fusion. Then, in order to

Motivated by lateral vibration suppression of a mining cable elevator, which is a load-moving cable system, where the load moves along a viscoelastic guideway whose stiffness and damping coefficients are unknown, we present event-triggered adaptive output-feedback boundary control design of a hyperbolic PDE–ODE coupled system using the measurements at the PDE actuated boundary and the ODE, where the

We provide new designs for multivariable extremum seeking for static maps with arbitrarily long time delays. We allow different delays in each input channel and output delays, and gradient- and Newton-based frameworks. Our sequential predictor based approach provides an alternative to earlier prediction methods, and leads to extremum seeking designs where a dynamical extension eliminates the need for

This paper presents a design method of terminal sliding mode control with the guaranteed attractiveness, for which the tracking performance of the closed-loop system undertaken is governed by a discretized finite-time system (FTS). To alleviate chattering effectively, this paper conducts the controller derivation according to the dead-beat reaching law that defines the sliding variable with the smoothing

The zero dynamics of linear systems with commensurate delays is studied. The zero dynamics is meant to be the dynamical equation that remains after compelling, by the input of the system, the system output to be identically equal to zero. Conditions are found out allowing to transform the system into a normal form that reveals its zero dynamics. By a straightforward use of the obtained decomposition

This paper describes a feedback controller design for chatter suppression in machining based on combined flexible structure and cutting force models. To deal with time-delayed feedback effects from cutting forces, a linear time-delay control system is introduced that satisfies an H∞ norm-bound criterion. A corresponding set of linear matrix inequalities (LMIs) is derived from a delay-dependent Lyapunov–Krasovskii

In this paper we propose a new state observer design technique for nonlinear systems. It consists of an extension of the recently introduced parameter estimation-based observer, which is applicable for systems verifying a particular algebraic constraint. In contrast to the previous observer, the new one avoids the need of implementing an open loop integration that may stymie its practical application

This paper investigates the consensus problem for nonlinear heterogeneous multi-agent systems with limited communication data rate. Each agent is modeled by a higher-order strict-feedback continuous-time system with unknown nonlinearities and external disturbance, and only the first state variable being measurable. Extended state observers (ESOs) are used to estimate the unmeasurable agent states and

This paper studies the bipartite consensus problem for a network of wave PDEs. The interactions among participating individuals may be antagonistic characterized by a signed digraph. Two distributed algorithms consisting of boundary sensing of the agents’ states are proposed for the disturbance-free and disturbed cases, respectively. Then Lyapunov function is constructed to assure bipartite consensus

In this paper, the attitude maneuver control without unwinding for rigid spacecraft is addressed based on the modified Rodrigues parameters (MRPs). First of all, an attitude control system model for rigid spacecraft is constructed by designing a selection logic for the MRP set and its shadow set. To guarantee the asymptotic stability in the large and anti-unwinding performance of the closed-loop system

In this paper, we provide novel conditions for stability analysis of aperiodically sampled nonlinear control systems subjected to time-varying delay. The proposed approach can also deal with cases in which delay is larger than the sampling interval. It is applicable to a general class of nonlinear systems and provides sufficient criteria for stability that aid in making trade-offs between control performance

This paper deals with the exponential stability analysis of decentralized, sampled-data, Linear Time Invariant (LTI) control systems with asynchronous sensors and actuators. We consider the case where each controller in the decentralized setting has its own sampling and actuation frequency, which translates to asynchrony between sensors and actuators. Additionally, asynchrony may be induced by delays

The inverse optimal control for finite-horizon discrete-time linear quadratic regulators is investigated in this paper, which is to estimate the parameters in the objective function using noisy measurements of partial optimal states only. By the Pontryagin’s minimum principle, the concerned inverse optimal control problem is recast as the identification of a parameterized causal-and-anticausal mixed

Chemical self-assembly has been considered one of the most important scientific problems in the 21st century; however, because the process of self-assembly is very complex, there is currently little mathematic theory describing it. Based on some assumptions of the classic hard sphere model, this paper presents a novel multi-agent system for chemical self-assembly that can be formulated as a group of

This paper studies an event-triggered distributed optimal coordination problem for heterogeneous linear multi-agent systems with external disturbances. A new distributed optimal coordination algorithm based on event-triggered communication is proposed to deal with heterogeneous agents having a relative degree two. By utilizing the input-to-state stability guaranteed by the proposed algorithm, a novel

We propose a method that simultaneously identifies a control-oriented model of a building’s temperature dynamics and a transformed version of the unmeasured disturbance affecting the building. Our method uses ℓ1-regularization to encourage the identified disturbance to be approximately sparse, which is motivated by the slowly-varying nature of occupancy that determines the disturbance. The proposed

In this paper, we propose a novel approach to the problem of augmenting distance-based formation controllers with a secondary constraint for the purpose of preventing 3D formation ambiguities. Specifically, we introduce three controlled variables that form an orthogonal space and uniquely characterize a tetrahedron formation in 3D. This orthogonal space incorporates constraints on the inter-agent distances

Max-Plus Algebra has been applied in several contexts, especially in the control of discrete events systems. In this article, we discuss another application closely related to control: the use of Max-Plus algebra concepts in the context of reinforcement learning. Max-Plus Algebra and reinforcement learning are strongly linked due to the latter’s dependence on the Bellman Equation which, in some cases

We address the problem of optimizing the performance of a dynamic system while satisfying hard safety constraints at all times. Implementing an optimal control solution is limited by the computational cost required to derive it in real time, especially when constraints become active, as well as the need to rely on simple linear dynamics, simple objective functions, and ignoring noise. The recently

In this work, we address the feedback stabilization problem for discrete-time reversible switched linear control systems. We focus on the question “how many state feedback gain matrices are needed for an nth order system with m subsystems?” We prove that n+m is an upper bound for the question. For completely controllable systems, a constructive design scheme is developed to steer any initial state

This paper develops a new model reference adaptive control (MRAC) framework using partial-state feedback for solving a multivariable adaptive output tracking problem. The developed MRAC scheme has full capability to deal with plant uncertainties for output tracking and has desired flexibility to combine the advantages of full-state feedback MRAC and output feedback MRAC. With such a new control scheme

This paper presents a new study on adaptive state feedback output tracking control problem for uncertain discrete-time nonlinear systems in a general non-canonical form. Time-advance operations on the output of such systems result in the output dynamics being nonlinearly dependent on the control input and unknown parameters, which leads to three technical issues: implicit relative degree; nonlinearly

We propose a distributed solution for a constrained convex optimization problem over a network of clustered agents each consisted of a set of subagents. The communication range of the clustered agents is such that they can form a connected undirected graph topology. The total cost in this optimization problem is the sum of the local convex costs of the subagents of each cluster. We seek a minimizer

In this paper we investigate how stability and optimality of consensus-based distributed filters depend on the number of consensus steps in a discrete-time setting for both directed and undirected graphs. By introducing two new algorithms, a simpler one based on dynamic averaging of the estimates and a more complex version where local error covariance matrices are exchanged as well, we are able to

The adaptive tracking state-feedback control for underactuated mechanical systems with outputs characterized by global relative degree equal to two is addressed. A new control technique called virtual control is proposed. Fictitious control inputs and outputs are introduced to achieve full actuation: the key step is to find the reference signals for the fictitious outputs so that the resulting virtual

Many recent dynamic programming specifications fail to satisfy traditional contractivity conditions, which are a cornerstone of the standard optimality theory for infinite horizon problems in discrete time. We formulate alternative conditions based around monotonicity and “value” convexity. These conditions lead to an optimality theory that is as strong as the contractive case. Several applications