Event-Triggered Containment Control for Nonlinear Multiagent Systems via Reinforcement Learning
Zichen Wang, Xin Wang, Chen Zhao
Abstract
Herein, we concern with the problem of event-triggered containment optimal control for a class of nonlinear multiagent discrete-time systems (MADSs). Compared to the previous containment control strategy for linear MADSs, a novel containment control strategy for nonlinear MADSs via the backstepping technique is introduced. Utilizing the classic reinforcement learning (RL) approach, we implement the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${n}$ </tex-math></inline-formula> -step backstepping structure with actor-critic neural networks (NNs). In addition, instead of a time-triggered scheme, the event-triggered scheme (ETS) is employed for saving computations. Finally, some simulation results are presented to verify the performance of our control strategy.