Event-Based Adaptive Optimal Fault-Tolerant Consensus Control for Uncertain Nonlinear Multiagent Systems With Actuator Failures

This article addresses the event-based adaptive optimal fault-tolerant consensus control problem for a class of nonlinear multiagent systems (MASs) under a directed graph. It can be solved that both control gains and actuator failure parameters are unknown in considered MASs, which enhances the practicability of optimal consensus control. First, a reinforcement learning-based distributed optimal control law is designed by constructing the identifier-critic-actor learning networks. Furthermore, by utilizing the distributed optimal control law as an auxiliary variable, an adaptive fault-tolerant controller is proposed to effectively compensate for actuator failures. Meanwhile, a co-design scheme is proposed for the construction of an event-triggered control input with the fault-tolerant property. It is proven that the designed control input ensures the boundedness of closed-loop systems through rigorous stability analysis. Finally, the effectiveness of the developed approach can be illustrated via simulations of numerical nonlinear MASs and a group of autonomous underwater vehicles.