Adaptive Neural Network Control for Consensus of Nonlinear Multi-Agent Systems with Actuator Faults

Abstract

This paper investigates the fault tolerant consensus problem for a class of nonlinear multi-agent systems with actuator faults. The dynamics of the multi-agent systems are unknown nonlinear and nonidentical. The types of actuator fault include partial loss of effectiveness fault and biased fault. The main idea of the fault tolerant control adopted in this paper is the adaptive control. The control method used is a neural network based adaptive control which has a better adaptability than the traditional adaptive control. The developed adaptive neural network consensus protocol is proved to perform well with respect to the system nonlinear dynamics and actuator faults of the agent. Finally, numerical simulation on multi-agent system of four Chen's chaotic systems is performed to illustrate the effectiveness of the investigated adaptive neural network consensus protocol.