This paper introduces a novel approach to solve the coverage optimization problem in multi-agent systems. The proposed technique offers an optimal solution with a lower cost with respect to conventional Voronoi-based techniques by effectively handling the issue of agents remaining stationary in regions void of information using a ranking function. The proposed approach leverages a novel cost function

This paper investigates functional interval estimation for continuous-time linear systems subject to both time-varying and time-invariant uncertainties. Two novel methods are proposed based on peak-to-peak functional observer design and interval analysis. First, we present a splitting-based method that splits the estimation error dynamics into two subsystems to handle the time-invariant disturbances

This paper addresses the leader-following consensus problem for general linear multiagent systems under general directed topology, with a focus on the scenarios where only relative output information is available. To solve such problem, most existing observer-based protocols rely on relative observer information among neighboring agents, which is obtained through the communication network and can be

This paper addresses the input-to-state stability (ISS) of infinite-dimensional systems by introducing a novel notion named generalized ISS-Lyapunov functional (GISS-LF) and the corresponding ISS Lyapunov theorem. Unlike the classical ISS-Lyapunov functional (ISS-LF) that must be positive definite, a GISS-LF can be positive semidefinite. Moreover, such a functional considers not only the relationship

This paper studies a finite horizon utility maximization problem on excessive consumption under a drawdown constraint. Our control problem is an extension of the one considered in Angoshtari et al. (2019) to the model with a finite horizon and an extension of the one considered in Jeon and Oh (2022) to the model with zero interest rate. Contrary to Angoshtari et al. (2019), we encounter a parabolic

Motivated by the increasing interest in DC microgrids, we study the distributed secondary control problem of DC microgrids which aims to simultaneously guarantee load current sharing and DC bus voltage restoration. In particular, we analyze in-depth the effects of nonlinear constant power loads (CPLs), and successfully establish an upper bound for CPLs to guarantee the stability of DC microgrids. This

This paper presents a novel approach to synthesize positive invariant sets for unmodeled nonlinear systems using direct data-driven techniques. The data-driven invariant sets are used to design a data-driven command governor that selects a command for the closed-loop system to enforce constraints. Using basis functions, we solve a semi-definite program to learn a sum-of-squares Lyapunov-like function

This note aims to identify and rectify the flaws found in the proof of Chen et al. (2023), specifically in Lemma 2, Lemma 3, Theorem 1, and Theorem 2. While the conclusions of Lemma 2, Lemma 3, and Theorem 1 remain valid, certain aspects of their proofs are found to be flawed. This note provides modifications to address these flaws. Additionally, the statement and proof of Theorem 2 are shown to be

One-shot direct model-reference control design techniques, like the Virtual Reference Feedback Tuning (VRFT) approach, offer time-saving solutions for calibrating fixed-structure controllers. Nonetheless, such methods are known to be highly sensitive to the quality of data, often requiring long and costly experiments to attain acceptable closed-loop performance. These features might prevent the widespread

We propose a general notion of dissipativity with dynamic supply rates for nonlinear systems. This extends classical dissipativity with static supply rates and dynamic supply rates of miscellaneous quadratic forms. The main results of this paper concern Lyapunov and asymptotic stability analysis for nonlinear feedback dissipative systems that are characterised by dissipation inequalities with respect

This paper investigates the problem of simultaneous state and unknown input estimation/observation within a prescribed time for a class of descriptor systems with unknown inputs (UIs). Firstly, for the descriptor system with UIs, a new structure decomposition is developed which provides a more straightforward way to estimate the state and UIs. Subsequently, based on the sub-system obtained by the decomposition

This article considers state estimation of rigid-body dynamics where the positions q are available to measurement but where the angular velocities q̇ or accelerations q̈ are not available to measurement. Using a stability-oriented approach to model-based design of state estimation for Euler–Lagrange systems and rigid-body dynamics, state estimation based on position measurement is shown to guarantee

The paper studies the safety verification problem for nonlinear systems and focuses on the converse problem of zeroing barrier functions (ZBFs). We establish two necessary and sufficient conditions for the existence of a ZBF by solving the converse ZBF problem. Moreover, we also consider exponential barrier functions (EBFs), a special case of the ZBF, and provide a necessary and sufficient condition

The zero-sum game-based stochastic encryption problem for cyber–physical systems (CPSs) under stealthy attacks is investigated in this paper. Firstly, a deterministic encryption strategy is presented to achieve the same estimation performance as the standard Kalman filter in the absence of attacks. Since a deterministic procedure may be deciphered, the encryption/attack strategy will be randomly employed

We study the problem of identifying Finite Impulse Response (FIR) systems against random replay attacks with binary-valued observations in this paper. Replay attacks are modeled and the impact of attack strategies on the performance of parameter estimation algorithms is investigated. A defense algorithm that is consistent even during attacks is designed, and the problem of identifiability for the unknown

This paper investigates the problem of diagnosing the occurrence of a fault event in a discrete event system (DES) subject to malicious attacks. We consider a DES monitored by an operator through the perceived sensor observations. It is assumed that an attacker can tamper with the sensor observations, and the system operator is not aware of the attacker’s presence at the beginning. We propose a stealthy

This paper introduces a heterogeneous multidimensional bounded confidence (BC) opinion dynamics with random pairwise interactions, whereby each pair of agents accesses each other’s opinions with a specific probability. This revised model is motivated by the observation that the standard Hegselmann–Krause (HK) dynamics requires unrealistic all-to-all interactions at certain configurations. For this

In this paper, several estimation algorithms called the variational unscented Kalman filters (UKF-Vs) are proposed for matrix Lie groups. The proposed filters are inspired by the unscented Kalman filter in Euclidean space and they exhibit advantages over conventional methods, as the prediction step and the measurement update step are established on the Lie algebra and its dual space, which therefore

Reduced-order identification algorithms are usually used in machine learning and big data technologies, where the large-scale systems widely exist. For large-scale system identification, traditional least squares algorithm involves high-order matrix inverse calculation, while traditional gradient descent algorithm has slow convergence rates. The reduced-order algorithm proposed in this paper has some

In this paper, we focus on developing a resilient stepped transmission scheme for nonlinear networked systems suffering from Denial-of-Service (DoS) attacks. Instead of employing static periodic sampling, the proposed acknowledgement-based adaptive sampler follows a resilient stepped algorithm to dynamically adjust the sampling period based on the absence or presence of DoS attacks. The dynamic sampling

This paper is concerned with the leaderless output consensus control problem for uncertain nonlinear multi-agent systems with mismatched unknown parameters. Different from currently available results, the considered agents are allowed to have heterogenous and arbitrary system orders. To solve such consensus problem, a novel distributed reference system, whose output serves as a local reference signal

This paper presents a novel model-free method to solve linear quadratic Gaussian mean field social control problems in the presence of multiplicative noise. The objective is to achieve a social optimum by solving two algebraic Riccati equations (AREs) and determining a mean field (MF) state, both without requiring prior knowledge of individual system dynamics for all agents. In the proposed approach

The inertial proximal method is extended to minimize the sum of a series of separable nonconvex and possibly nonsmooth objective functions and a smooth nonseparable function (possibly nonconvex). Here, we propose two new algorithms. The first one is an inertial proximal coordinate subgradient algorithm, which updates the variables by employing the proximal subgradients of each separable function at

This paper derives necessary and sufficient (N&S) conditions for the exponential stability of discrete-time switched positive linear systems under transfer-restricted switching. The transfer-restricted switching property is characterized by a switching digraph, and the structural properties for subsystems are characterized by a novel class of state component digraphs. Combining with the two properties

This paper focuses on the motion planning for mobile robots in 3D, which are modeled by 6-DOF rigid body systems with nonholonomic kinematics constraints. We not only specify the target position, but also impose the requirement of the heading direction at the terminal time, which gives rise to a new and more challenging 3D motion planning problem. The proposed planning algorithm involves a novel velocity

Cyber–physical systems (CPS) play a pivotal role in numerous critical real-world applications that have stringent requirements for safety. To enhance the CPS resiliency against attacks, redundancy can be integrated in real-time controller implementations by designing strategies that switch among multiple controllers. However, existing switching strategies typically overlook remediation measures for

The issue of filter stability with respect to (w.r.t.) the initial condition refers to the unreliable filtering process caused by improper prior information of the initial state. This paper focuses on analyzing and resolving the stability issue w.r.t. the initial condition of the classical Set-Membership Filters (SMFs) for linear time-invariant systems, which has not yet been well understood in the

Robust control of quantum systems is an increasingly relevant field of study amidst the second quantum revolution, but there remains a gap between taming quantum physics and robust control in its modern analytical form that culminated in fundamental performance bounds. With certain exceptions such as quantum optical systems that can be modeled as linear stochastic differential equations, quantum systems

This paper investigates event-triggered stabilization of uncertain switched linear systems under finite feedback bit rate where the boundary of model uncertainty is unknown. Based on the appropriate boundary estimation, a novel communication and control strategy is given to obtain qualified feedback gains and visible system states in which the event-triggering mechanism is adopted. By utilizing the

The training of neural networks can be formulated as an optimal control problem of a dynamical system. The initial conditions of the dynamical system are given by the data. The objective of the control problem is to transform the initial conditions in a form that can be easily classified or regressed using linear methods. This link between optimal control of dynamical systems and neural networks has

This paper analyzes finite-state Markov Decision Processes (MDPs) with nonstationary and uncertain parameters via set-based fixed point theory. Given compact parameter ambiguity sets, we demonstrate that a family of contraction operators, including the Bellman operator and the policy evaluation operator, can be extended to set-based contraction operators with a unique fixed point—a compact value function

A key challenge in solving the deterministic inverse reinforcement learning (IRL) problem online and in real-time is the existence of multiple solutions. Nonuniqueness necessitates the study of the notion of equivalent solutions, i.e., solutions that result in a different cost functional but same feedback matrix. While offline algorithms that result in convergence to equivalent solutions have been

This paper aims to solve the model-free linear quadratic regulator problem for discrete-time Markov jump linear systems without requiring an initial stabilizing control policy. We propose both model-based and model-free successive over relaxation algorithms to learn the optimal control policy of discrete-time Markov jump linear systems. The model-free value iteration algorithm is a special case of

This paper investigates the vulnerability of heterogeneous multi-agent systems (MASs) in face of perfect false-data injection (FDI) attacks that stealthily destabilize the synchronization processes of agents. In contrast to homogeneous dynamics, heterogeneous dynamics can be asymmetrically worsened by attackers, which is a greater challenge for MAS security. First of all, the existence conditions of

This paper considers the problems of stabilization and H∞ control for fast sampling discrete-time singularly perturbed singular Markovian systems (SPSMSs). The system equivalent approach is initially introduced to transform the discrete fast sampling SPSMS model into the augmented SPSMS for the convenience of designing system controller. Secondly, sufficient condition on stochastically mean square

Approximate methods to solve stochastic optimal control (SOC) problems have received significant interest from researchers in the past decade. Probabilistic inference approaches to SOC have been developed to solve nonlinear quadratic Gaussian problems. In this work, we propose an Expectation–Maximization (EM) based inference procedure to generate state-feedback controls for constrained SOC problems

Barrier function-based adaptive sliding mode control (BFASMC) is analyzed in presence of deterministic measurement noise. It is shown that, considering only boundedness of the measurement noise, it is impossible to select the controller parameters to track some perturbation with unknown bound. Nonetheless, under the assumption of continuity of the noise, the tracking of such a perturbation is possible;

This paper investigates the stability issues of model predictive control (MPC) for discrete-time linear systems with state and finite control set (FCS) constraints subject to time-varying disturbances. A new FCS-MPC design and analysis framework is developed using the disturbance estimation approaches and the tool of robust positive invariant (RPI) set sequence. It encompasses a discrete-time exogenous

This paper solves the problem of how to control a chain of integrators with unknown delays in both state and input by memoryless state feedback. Inspired by the non-identifier based adaptive control scheme (Lei and Lin, 2006) as well as the recent progress in stabilizing time-delay feedforward systems with unknown parameters via dynamic state compensation (Sun and Lin, 2023), we design a memoryless

In this paper we extend our recently proposed Deadzone-Adapted Disturbance Suppression (DADS) Control approach from systems with matched uncertainties to general systems in parametric strict feedback form. The DADS approach prevents gain and state drift regardless of the size of the disturbance and unknown parameter and achieves an attenuation of the plant output to an assignable small level, despite

Plan-based coordination, which can fill the vacancy of control during the communication silent periods, improves the ability of agents’ independence and reduces the requirement of communication resources. This paper presents a generalized form of plan-based coordination, namely the time-extended consensus problem, which is applicable to multi-agent systems entering non-interaction state. Conditions

We consider the design of state feedback control laws for both the switching signal and the continuous input of an unknown switched linear system, given past noisy input-state trajectories measurements. Based on Lyapunov–Metzler inequalities and on a matrix S-lemma, we derive data-dependent bilinear programs, whose solution directly returns a provably stabilizing controller and ensures H2 or H∞ performance

Optimizing a parameterized zonotope or ellipsoid is a common task in robust state estimation, fault diagnosis and reachability analysis. Recent studies have unified ellipsoids and zonotopes into basic ellipsotopes, which support precise representation for more nonlinear boundaries and constraints in practical applications. However, there are currently no available optimality criteria and optimization

We study a pair of budget- and performance-constrained weak submodular maximization problems. For computational efficiency, we explore the use of stochastic greedy algorithms which limit the search space via random sampling instead of the standard greedy procedure which explores the entire feasible search space. We propose a pair of stochastic greedy algorithms, namely, Modified Randomized Greedy (MRG)

For “Timed Event Graphs”, linear equations can be written in the max-plus algebra that describe the firing dynamics. In some cases, independent events/inputs can be used to influence the system dynamics in order to achieve a desired specification. In this setting, significant attention has been devoted to the mathematical development of controllers that achieve these desired specifications. In this

The feedforward control strategy exhibits substantial capability and high-precision control for output tracking tasks. However, the feedforward control action obtained through solving the inverse problem is unstable for non-minimum phase systems. In this paper, a novel stable inversion method is presented, termed lifted time stable inversion. Compared to the existing method, the proposed method does

This paper proposes a hierarchical control approach to solve the prescribed performance lane-changing control problem for nonlinear vehicle platoons. By incorporating the collision avoidance and comfort assurance conditions specifically tailored for vehicle platoons, the value range of the safe lane-changing completion time (LCCT) can be calculated. Based on which, the longitudinal and lateral reference

Linear Quadratic differential games and their Open-Loop Nash Equilibrium (OL-NE) strategies are studied with a threefold objective. First, it is shown that the state/costate lifted system (arising from the application of Pontryagin’s Minimum Principle) is such that its behaviour restricted to the equilibrium subspace can be interpreted as the (non-power-preserving) interconnection of two cyclo-passive

Despite the recent development of prescribed-time control theory, the highly desirable separation principle remains unavailable for nonlinear systems with only the output being measurable. In this paper, for the first time we establish such separation principle for a class of nonlinear systems, such that the prescribed-time observer and prescribed-time controller can be designed independently, and

This paper presents a comprehensive convergence analysis for the mirror descent (MD) method, a widely used algorithm in convex optimization. The key feature of this algorithm is that it provides a generalization of classical gradient-based methods via the use of generalized distance-like functions, which are formulated using the Bregman divergence. Establishing convergence rate bounds for this algorithm

This paper addresses fault diagnosis and tampering detection in discrete event systems modeled with nondeterministic finite automata under malicious attacks. We propose a novel structure to simultaneously track the occurrence of fault events and tampering actions in systems compromised by attacks (i.e., by arbitrary deletions, insertions, or substitutions of observed symbols). Assuming that each deletion

We consider the design of functional estimators, i.e., approaches to compute an estimate of a nonlinear function of the state of a general nonlinear dynamical system subject to process noise based on noisy output measurements. To this end, we introduce a novel functional detectability notion in the form of incremental input/output-to-output stability (δ-IOOS). We show that δ-IOOS is a necessary condition

We propose a novel adaptive learning-based model predictive control (MPC) scheme for interconnected systems which can be decomposed into several smaller dynamically coupled subsystems with uncertain coupling. The proposed scheme is mainly divided into two main online phases; a learning phase and an adaptation phase. Set membership identification is used in the learning phase to learn an uncertainty

This paper presents two new control approaches for guaranteed safety (remaining in a safe set) subject to actuator constraints (the control is in a convex polytope). The control signals are computed using real-time optimization, including linear and quadratic programs subject to affine constraints, which are shown to be feasible. The first control method relies on a soft-minimum barrier function that

This study presents a numerical method for the optimal control problem with equilibrium constraints (OCPEC). It is extremely difficult to solve OCPEC owing to the absence of constraint regularity and strictly feasible interior points. To solve OCPEC efficiently, we first relax the discretized OCPEC to recover the constraint regularity and then map its Karush–Kuhn–Tucker (KKT) conditions into a parameterized

If a linear switching system with frequent switches is stable, will it be stable under arbitrary switches? In general, the answer is negative. Nevertheless, this question can be answered in an explicit form for any concrete system. This is done by finding the mode-dependent critical lengths of switching intervals after which any enlargement does not influence the stability. The solution is given in

This paper proposes a nonlinear estimator for the robust reconstruction of process and sensor faults for a class of uncertain nonlinear systems. The proposed fault estimation method augments the system dynamics with an ultra-local (in time) internal state–space representation (a finite chain of integrators) of the fault vector. Next, a nonlinear state observer is designed based on the known parts of

This paper studies the distributed H∞ filtering problem for interconnected large-scale systems (ILSs). In distributed filtering, all sub-filters are interconnected via the designed interconnection matrix and each filter only requires local subsystems’ measurements to estimate the target signals. By the developed space construction method, novel both necessary and sufficient conditions are proposed