Event-Triggered Optimal Containment Control for Heterogeneous Stochastic Nonlinear Multiagent Systems Under Denial-of-Service Attacks

Ever since the reinforcement learning (RL) method was proposed, the optimal control problem for multiagent systems (MASs) has been intensively explored in light of the limitation of the control resource. However, most of the consequences have overlooked the denial-of-service (DoS) attacks which are often encountered in engineering scenarios. Thus, the current investigation makes the first attempt to explore the optimized containment control issue with a dynamic event-triggered mechanism for heterogeneous stochastic MASs subject to DoS attacks. For the purpose of achieving optimal control, the optimized backstepping technique is developed by resorting to a simplified RL algorithm based on the identifier–critic–actor structure. Then, a novel dynamic event-triggered mechanism is put forward to update the control input signals only at triggering instants so as to reduce the communication burden. Furthermore, by means of stochastic Lyapunov stability theory, it is verified that all signals in the closed-loop system are cooperatively semi-globally uniformly ultimately bounded in probability, in the simultaneous presence of disturbances and DoS attacks. Finally, the validation of the presented strategy is demonstrated via a simulation example.