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This article considers the identification of finite-impulse response systems, where information about the inputs and outputs of the system undergoes quantization into binary values before transmission to the estimator. In the case where the thresholds of the input and output quantizers can be adapted, we propose identification schemes that are strongly consistent for Gaussian distributed inputs and

The article is concerned with active disturbance rejection control of a heat equation. The considered heat equation satisfies the Dirichlet boundary condition on one part of the boundary. On the other part of the boundary is located a Neumann boundary control. The heat equation system suffers from both a model uncertainty in the heat flow modeling and an unknown external disturbance. Our control approach

To study optimal control and disturbance attenuation problems, two prominent—and somewhat alternative—strategies have emerged in the last century: dynamic programming (DP) and Pontryagin's minimum principle (PMP). The former characterizes the solution by shaping the dynamics in a closed loop ( a priori unknown) via the selection of a feedback input, at the price, however, of the solution to (typically

Recent results have shown that, for continuous-time systems obtained by cascading an asymptotically stable system with an inner system, the first $n$ Hankel singular values are greater than or equal to those of the original system. Similarly, cascading a system in minimal form with an inner system, the same property holds for the linear quadratic Gaussian (LQG) characteristic values. In this article

The invariant-set motion planner uses a collection of safe sets to find a collision-free path through an obstacle-filled environment [1] – [4] . This article extends the invariant-set motion planner to systems with persistently varying disturbances and parametric model uncertainty. This is accomplished by replacing the previously used positive invariant sets with robust positive invariant sets. Since

This article presents a unified framework for filtering and control of quaternion-valued state vector processes through multiagent networked systems. To achieve this goal, the filtering problem in sensor networks is revisited, where a distributed Kalman filtering algorithm for filtering/tracking quaternion-valued state vector processes is developed. The distributed quaternion Kalman filter is formulated

In this article, a new event-triggered $H_{i}/H_{\infty }$ optimization fault detection (FD) scheme is proposed for linear discrete time systems. A linear discrete time system with varying sampling periods is first presented to describe the dynamics of an event-triggered system. Based on this, an observer-based fault detection filter (FDF) is constructed as an event-triggered residual generator so

In this article, an upper bound is established for the estimation error of a standard least squares (LS) algorithm used to identify a continuous-time model from filtered, sampled input–output data. It is found that the error has three constituent components due to the initial conditions, observation noise, and sample period. In particular, the initial condition bias is bounded by $O(1/[N\Delta t])$

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This article investigates the fixed-time leader-following consensus problem for high-order nonlinear multiagent systems dominated by the lower triangular model with a time-varying gain. Based on the fixed-time distributed observer and dynamic gain control approach, a new dynamic controller is developed with two online tuned gains such that the fixed-time leader-following consensus is achieved. It is

In this article, we address the design and analysis of multivariable Newton-based extremum seeking (ES) for static locally quadratic maps subject to actuation dynamics governed by diffusion partial differential equations (PDEs). Multi-input systems with distinct diffusion coefficients in each individual input channel are dealt with. The phase compensation of the dither signals is handled as a trajectory

This article studies the adaptive state feedback stabilization problem of stochastic nonlinear systems with many uncertainties, such as parametric uncertainties, time-varying delay, unknown powers, and stochastic inverse dynamics. By virtue of the adding-a-power-integrator technique, an adaptive control scheme together with a novel state feedback control law is proposed to deal with these unknowns

Goal-driven selective attention (GDSA) refers to the brain's function of prioritizing the activity of a task-relevant subset of its overall network to efficiently process relevant information while inhibiting the effects of distractions. Despite decades of research in neuroscience, a comprehensive understanding of GDSA is still lacking. We propose a novel framework using concepts and tools from control

This article deals with output feedback selection in linear time-invariant structured systems. We assume that the inputs and the outputs are dedicated , i.e., each input directly actuates a single state and each output directly senses a single state. Given a structured system with dedicated inputs and outputs and a cost matrix that denotes the cost of each feedback connection, our aim is to select

We study the reduction of degrees of freedom for the equations that determine necessary optimality conditions for extrema in an optimal control problem for a multiagent system by exploiting the physical symmetries of agents, where the kinematics of each agent is given by a left-invariant control system. Reduced optimality conditions are obtained using techniques from variational calculus and Lagrangian

This article is concerned with the exponential stabilization of a class of linear boundary control systems (BCSs) in port-Hamiltonian form through energy shaping. Starting from a first feedback loop that is in charge of modifying the Hamiltonian function of the plant, a second control loop that guarantees exponential convergence to the equilibrium is designed. In this way, a major limitation of standard

One of the modern research lines in econometrics studies focuses on translating a wide variety of structural econometric models into their state-space form, which allows for efficient unknown dynamic system state and parameter estimations by the Kalman filtering scheme. The mentioned trend yields advanced state-space model structures, which demand innovative estimation techniques driven by application

Motivated by challenges in feedback control with event-triggered control input updates, this article studies an ellipsoidal set-valued feedback control problem for linear time-invariant (LTI) discrete-time (DT) systems, and its application to event-triggered control systems. First, a state feedback controller based on the set-valued information is designed for LTI DT systems with disturbances. Then

This article studies the remote state estimation problem of linear time-invariant systems with stochastic event-triggered sensor schedules in the presence of packet drops between the sensor and the estimator. Due to the existence of packet drops, the Gaussianity at the estimator side no longer holds. It is proved that the system state conditioned on the available information at the estimator side is

The Lyapunov–Krasovskii functional (LKF) method is an effective tool to obtain delay-dependent stability criteria of time-varying delay systems. However, in the method, the requirement that there must exist an appropriate LKF for a system with a delay varying in a delay interval is too strict and may lead to considerable conservativeness. In order to get less conservative stability criteria, a novel

Distributed detection of covert attacks for linear large-scale interconnected systems is addressed in this article. Existing results consider the problem in centralized settings. This article focuses on large-scale systems subject to bounded process and measurement disturbances, where a single subsystem is under a covert attack. A detection methodology is proposed, where each subsystem can detect the

This article proposes a novel information-theoretic joint probabilistic data association filter for tracking unknown number of targets. The proposed information-theoretic joint probabilistic data association algorithm is obtained by the minimization of a weighted reverse Kullback–Leibler divergence to approximate the posterior Gaussian mixture probability density function. Theoretical analysis of mean

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

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

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

Goal-driven selective attention (GDSA) is a remarkable function that allows the complex dynamical networks of the brain to support coherent perception and cognition. Part I of this two-part article proposes a new control-theoretic framework, termed hierarchical selective recruitment (HSR), to rigorously explain the emergence of GDSA from the brain's network structure and dynamics. This part completes

Robust global stabilization of nonlinear systems by observer-based feedback controllers is a challenging task. This article investigates the problem of designing observer-based stabilizing controllers for incrementally quadratic nonlinear systems with external disturbances. The nonlinearities considered in the system model satisfy the incremental quadratic constraints, which are characterized by incremental

In genetic regulatory networks (GRNs), gene mutations often occur in a stochastic manner. As an important model of GRNs, gene mutations of Boolean networks are always described as function perturbations. This article studies the robust stability and stabilization of Boolean networks with stochastic function perturbations. A kind of parameterized set is constructed, and it is revealed that under the

We propose a new robust recursive fixed-interval smoother for nonlinear systems under non-Gaussian process and measurement noises, i.e., the nominal Gaussian noise is polluted by large noise from unknown distributions. Taking advantage of correntropy in handling non-Gaussian noise, a robust Rauch–Tung–Striebel smoother is derived according to the maximum-correntropy-criterion-based cost functions with

This article proposes a method that combines linear quadratic Gaussian (LQG) control design and structure preserving model reduction for the reduced order control of infinite dimensional port Hamiltonian systems (IDPHS).For that purpose the weighting operators used in LQG control design are chosen such that the resulting dynamic controller is passive and the closed-loop system equivalent to control

We consider time synchronization attack against multisystem scheduling in a remote state estimation scenario where a number of sensors monitor different linear dynamical processes and schedule their transmissions through a shared collision channel. We show that by randomly injecting relative time offsets on the sensors, the malicious attacker is able to make the expected estimation error covariance

This article studies novel attack and defense strategies, based on a class of stealthy attacks, namely the zero-dynamics attack (ZDA), for multiagent control systems. ZDA poses a formidable security challenge since its attack signal is hidden in the null space of the state-space representation of the control system and hence it can evade conventional detection methods. An intuitive defense strategy

In this article, we will study a class of forward and backward stochastic difference equations (discrete-time FBSDEs) with time delay. By establishing a nonhomogeneous relationship between the forward and backward stochastic processes, we give the explicit solution of the discrete-time FBSDEs with time delay in terms of a class of Riccati-like equations. The explicit solution is of great significance

In this article, a novel second order sliding mode control strategy is proposed for relative-degree-2 nonlinear uncertain single- input–single-output (SISO) systems. To design the strategy, the phase portrait of the second order system in normal form associated with the formulated sliding mode control problem is studied. A sliding surface switching between arcs of parabolas is conceived to ensure the

This article deals with computational complexity of various problems related to the zero controllability of a discrete-time linear time-invariant system, assuming that purely structural conditions at the level of the connections between the system states (i.e., state-connections) and the connections from the inputs to the states (i.e., input-connections) are known. Given a generically zero controllable

In this article, we deal with the detectability problem for unambiguous weighted automata (UWAs). The problem is to determine if, after a finite number of observations, the set of possible states is reduced to a singleton. Four types of detectabilities, namely, strong detectability, detectability, strong periodic detectability, and periodic detectability are defined in terms of different requirements

Due to their relevance in controller design, we consider the problem of determining the $\mathcal {L}^2$ -gain, passivity properties, and conic relations of an input–output system. While, in practice, the input–output relation is often undisclosed, input–output data tuples can be sampled by performing (numerical) experiments. Hence, we present sampling strategies for discrete time and continuous time

This article addresses the behavior analysis problems for directed signed networks that involve cooperative–antagonistic interactions among agents. Of particular interest is to explore the convergence behaviors of directed signed networks with agents of mixed first-order and second-order dynamics. Furthermore, the agents are subject to nonidentical topologies represented by two different signed digraphs

We study stochastic static teams with countably infinite number of decision makers (DMs), with the goal of obtaining (globally) optimal policies under a decentralized information structure. We present sufficient conditions to connect the concepts of team optimality and person-by-person optimality for static teams with countably infinite number of DMs. We show that under uniform integrability and uniform

In this article, we present a novel distributed adaptive output feedback control for the cooperative output regulation problem of linear multiagent systems. The agent plant is general in the sense that those interesting practical issues such as unknown dynamics, directed network graph, and agents with nonidentical relative degrees are taken into account simultaneously. The obtained controller consists

This article deals with a class of resistive–inductive–capacitive (RLC) circuits and switched RLC (s–RLC) circuits modeled in the Brayton–Moser framework. For this class of systems, new passivity properties using a Krasovskii-type Lyapunov function as storage function are presented, where the supply rate is function of the system states, inputs, and their first time derivatives. Moreover, after showing

In this article, we study the nonlinear Fokker–Planck (FP) equation that arises as a mean-field (macroscopic) approximation of bounded confidence opinion dynamics, where opinions are influenced by environmental noises and opinions of radicals (stubborn individuals). The distribution of radical opinions serves as an infinite-dimensional exogenous input to the FP equation, visibly influencing the steady

The increasing penetration of renewable and distributed energy resources in distribution networks calls for real-time and distributed voltage control. In this article, we investigate local Volt/VAR control with a general class of control functions, and show that the power system dynamics with nonincremental local voltage control can be seen as a distributed algorithm for solving a well-defined optimization

This article focuses on distributed convex optimization problems over an unbalanced directed multiagent (no central coordinator) network with inequality constraints. The goal is to cooperatively minimize the sum of all locally known convex cost functions. Every single agent in the network only knows its local objective function and local inequality constraint, and is constrained to a privately known

In this article, we propose a region-based self-triggered control (STC) scheme for nonlinear systems. The state space is partitioned into a finite number of regions, each of which is associated to a uniform interevent time. The controller, at each sampling time instant, checks to which region does the current state belong, and correspondingly decides the next sampling time instant. To derive the regions

This article employs the observer-based output feedback control technique to deal with dynamic compensator design for a linear $N$ -D parabolic partial differential equation (PDE) with multiple local piecewise control inputs and multiple noncollocated local piecewise observation outputs. These control inputs and observation outputs are provided by only few actuators and noncollocated sensors active

In this article, we investigate both over- and under-approximations of reachable sets for analytic autonomous dynamical systems beyond polynomial dynamics. We start with the concept of evolution function, whose subzero-level set can be used to describe reachable set, and find a series representation of the evolution function with its Lie derivatives. Afterwards, based on the partial sums of this series

We develop backstepping state feedback control to stabilize a moving shockwave in a freeway segment under bilateral boundary actuations of traffic flow. A moving shockwave, consisting of light traffic upstream of the shockwave and heavy traffic downstream, is usually caused by changes of local road situations. The density discontinuity travels upstream and drivers caught in the shockwave experience

As a quantitative criterion for privacy of “mechanisms” in the form of data-generating processes, the concept of differential privacy was first proposed in computer science and has later been applied to linear dynamical systems. However, differential privacy has not been studied in depth together with other properties of dynamical systems, and it has not been fully utilized for controller design. In

In the standard linear, time-invariant feedback system, controllers that achieve diagonal decoupling and closed-loop stability exist if and only if the plant satisfies the diagonal denominator condition or has no coinciding poles and zeros in the region of instability. A simple and systematic decoupling design procedure is presented under each of these conditions. The closed-loop poles can be placed