Event-Triggered Containment Control for Nonlinear Multiagent Systems via Reinforcement Learning

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 ${n}$ -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.