IEEE Transactions on Automatic Control

This paper addresses theoretical limitations in Lyapunov-Krasovskii methods for time-varying delay systems through three principal contributions. First, a novel delay-dependent inequality for the derivatives of the Lyapunov function is proposed, which fundamentally distinguishes our approach from classical Halanay-type inequalities through explicit time-varying parameterization. An enhanced presc…

In this paper, we explore deception mechanisms in the context of game-theoretic learning. We first examine a scenario where certain agents, referred to as “intelligent players” (IP), given that they have increased intelligence, attempt to discern the scores of the learning mechanisms used by agents of lower intelligence, referred to as agents. To counteract this, agents alter the values of the sc…

This paper highlights an apparent, yet relatively unknown link between algorithm design in optimization theory and controller synthesis in robust control. Specifically, quadratic optimization can be recast as a regulation problem within the framework of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {H…

Real-time control of sensor networks under communication constraints has broad applications, including target tracking and mobile robotics. Traditional centralized methods face congestion and delay issues as system size grows, motivating a shift toward decentralized multi-agent approaches. This paper studies a nonstationary mean-risk optimization model for distributed sensor networks with distanc…

This paper considers distributed online nonconvex optimization with time-varying inequality constraints, where the nonconvex local loss and convex local constraint functions can vary arbitrarily across iterations. For a time-varying directed graph, we propose two distributed bandit online primal–dual algorithms with compressed communication to efficiently utilize communication resources in the on…

Social-psychological has provided clear evidence that opinion dynamics and decision making pro cesses are closely intertwined, yet the social phenomenon of delayed decision making in opinion evolution is ubiquitous. However, how to theoretically analyze the opinion dynamics with delayed decision making remains a challenging problem. In this paper, we propose a rigorous theoretical framework for a…

The necessary and sufficient conditions for distinguishing between two switched linear dynamical networks through trajectories are investigated. Three discernibility concepts are originally proposed, addressing two critical components of switched systems: switching instants and system topologies. Firstly, the necessary and sufficient discernibility condition is established for systems with identi…

In this article, we revisit a communication control co-design problem for a class of two-player stochas tic differential games on an infinite horizon. Each ‘player’ represents two active decision makers, namely a scheduler and a remote controller, which cooperate to optimize over a global objective while competing with the other player. Each player's scheduler can only intermittently relay state …

This paper is concerned with the design of online data-driven model predictive control (MPC) for switched linear systems with online-generated subsystems and unknowns witching signals. The online input-state data are used to detect switching instants, and update the MPC law directly to match the new subsystem bypassing identifying its model. Once a switching is detected, a fast-to slow excitation…

In this paper, we propose a new compression technique named Common randOm REconstruction (CORE), which can be used to compress the information transmitted between agents to reduce the total communication costs (TCCs) without other strict conditions. Previous communication compression techniques cannot ensure the improvement of TCCs due to the additional communication rounds caused by information …

We study vector versions of Halanay's inequality. We provide an input-to-state stability result. Even in the scalar case, we provide tighter bounds than those that were available in the literature. Our key new techniques involve finding componentwise positive vectors in the null spaces of suitable matrices. These matrices are expressible as weighted sums involving the coefficient matrices that oc…

This paper studies a distributed online optimization problem over partially Free-In and Free-Out (FIFO) networks, in which a set of unfixed agents cooperate to minimize the sum of a group of time-varying functions over a time horizon. To be specific, the agents are divided into static agents and dynamic agents. The static agents are those who remain in the network during the whole time horizon, w…

This paper proposes and rigorously develops a reinforcement learning(RL)-based optimized control strategy with notable time-synchronized stability (TSS) properties for zero-sum differential games. The proposed method addresses the challenge of approximating the time-synchronized Nash equilibrium solutions in nonlinear systems governed by the Hamilton-Jacobi-Isaacs (HJI) equation. By incorporating…

In the field of distributed online convex optimization with delayed feedback, previous works have primarily focused on scenarios with fixed and predetermined delays. In this paper, we consider a more complex and realistic scenario with unknown delays. Here the delayed periods vary among agents and iterations, and even the time stamps could be unknown. To address the challenges arising from asynch…

We consider finite Markov decision processes (MDPs) with convex constraints and known dynamics. In principle, this problem is amenable to off-the-shelf convex optimization solvers, but typically this approach suffers from poor scalability. In this work, we develop a first-order algorithm, based on the Douglas-Rachford splitting, that allows us to decompose the dynamics and constraints. Thanks to …

This paper addresses robust stability analysis for moving horizon estimation (MHE) in nonlinear systems and proposes new conditions for tuning the MHE cost function parameters. These conditions are directly linked to the MHE window size and the incremental exponential input/output-to-state stability (i-EIOSS) coefficients of the system. Our approach begins by introducing a general constraint equa…

In existing event-triggered sliding mode control (ETSMC) for switched systems, triggering or switching behavior usually causes the system states to deviate from the sliding surface, which may affect both stability and robustness of the system. To deal with this issue, this paper proposes a reachability-guaranteed ETSMC for switched systems without/with communication delay. Firstly, an event-trigg…

We study the problem of joint optimization involving coding and control policies for a controlled Markovian system over a finite-rate noiseless communication channel. While structural results on the optimal encoding and control have been obtained in the literature, their implementation has been prohibitive in general, except for linear models. To this end, we first develop existence, regularity, …

Network partitioning has gained recent attention as a pathway to enable decentralized operation and control in large-scale systems. This paper addresses the interplay between partitioning, observability, and sensor placement (SP) in dynamic networks. The problem, being computationally intractable at scale, is a largely unexplored, open problem in the literature. To that end, the paper's objective…