Robust Bipartite Consensus Tracking for Multi-Agent Systems under Acuator Faults

Abstract

This paper focuses on the robust bipartite consensus tracking problem of multi-agent systems under actuator faults. Both uncertainties and multiplicative actuator faults are considered in the system model. First, linearly parameterized neural networks are applied to approximate the uncertainty parts. Then, distributed adaptive update laws are designed for the unknown parameters and the actuator fault factors to guarantee the boundedness of estimation error. An adaptive bipartite consensus tracking control algorithm is proposed, which can solve the robust problem and suppress the uncertainties and actuator faults, simultaneously. Furthermore, numerical simulations are given to illustrate the effectiveness of the theoretical analysis.