Adaptive neural networks event-triggered fault-tolerant consensus control for a class of nonlinear multi-agent systems

This paper studies the event-triggered fault-tolerant control problem of nonlinear multiagent systems. The goal is to ensure the stability of event-based sampling multi-agent systems when the actuator faults occurs. The neural networks approximate property is used to approximate unknown ideal control parameters, which can reduce the exact requirements of control parameters. Based on the states information of neighboring agents, a distributed fault-tolerant consensus controller is designed for the leaderless multi-agent systems. Moreover, an event-triggered mechanism with special definition of event-triggered error is applied to reduce the amount of communications. In addition, the Zeno behaviour is avoided. By using Lyapunov stability theory, it is proved that all signals are bounded in the closed-loop systems. Finally, a numerical simulation result is presented to prove the effectiveness of the proposed method.