Distributed adaptive neural network consensus for a class of uncertain nonaffine nonlinear multi-agent systems

Abstract

This paper considers the distributed adaptive neural consensus tracking control problem for a class of uncertain nonaffine nonlinear multi-agent systems. By making use of the Taylor expansion technique, the nonaffine nonlinear control input of each subsystem is successfully separated under a weaker decoupling condition, and then, the distributed adaptive control is developed via neural networks (NNs) technique. By introducing the compensation adaptive laws with positive time-varying integrable functions to effectively handle the disturbances and the NN approximation errors in backstepping design process, a new distributed adaptive neural controller is constructed by means of the local output tracking error information of neighborhood agents. It can be proved that all the subsystem outputs asymptotically track to a desired reference trajectory. The efficiency of the established control strategy is demonstrated by the simulation experiment.