Consensus control of leader-following nonlinear multi-agent systems with distributed adaptive iterative learning control

This paper addresses the consensus problem of leader-following nonlinear multi-agent systems with iterative learning control. The assumption that only a small portion of following agents can receive the information of leader agent is considered. To approximate the nonlinear dynamics of a given system, the radial basis function neural network is introduced. Then, a distributed adaptive iterative learning control protocol with an auxiliary control term is designed, where the estimates of nonlinear dynamics are applied in control protocol design and three adaptive laws are presented. Furthermore, the convergence of the proposed control protocol is analysed by Lyapunov stability theory. Finally, a simulation example is provided to demonstrate the validity of theoretical results.