Reinforcement Learning-Based Event-Triggered Constrained Containment Control for Perturbed Multiagent Systems

Abstract

This article investigates the full-state-constrained optimal containment control problem of perturbed nonlinear multiagent systems (MASs). Initially, to balance control accuracy and cost while maintaining the states of MASs within confined regions, an enhanced constrained optimized backstepping (OB) framework is first developed for the multiagent control scenario by adopting an identifier-actor-critic-based reinforcement learning (RL) algorithm, where a novel performance index based on the barrier Lyapunov function (BLF) is integrated into the classic OB framework. Then, to enhance the robustness of the systems, the proposed framework employs disturbance observers to mitigate the effects of unknown external disturbances. Moreover, sufficient conditions are established to ensure that systems maintain stability and expected performance under denial-of-service (DoS) attacks. Subsequently, the controller implements a novel dynamic event-triggered mechanism (DETM), adaptively adjusting the triggering conditions by the estimated neural network (NN) weights in the proposed framework for substantial communication burden reduction. Finally, the stability of the systems is demonstrated using the Lyapunov theory, and a simulation example confirms the feasibility of the proposed scheme.