Distributed adaptive repetitive consensus control framework for uncertain nonlinear leader-follower multi-agent systems

In this paper, we propose an adaptive repetitive control framework for uncertain nonlinear multi-agent systems. Based on the framework, by learning periodic uncertainties, consensus-based learning control protocols are designed for nonlinear multi-agent systems with time-varying parametric uncertainty. The learning-based updating law is utilized to compensate for periodic time-varying parametric uncertainties. With the dynamic of the leader unknown to any follower agents, a new auxiliary control is designed for each follower agent to deal with the leader's dynamic. Then, the proposed learning control protocol guarantees that all follower agents can track the leader. Furthermore, as an extension of the consensus problem, the formation problem is studied. Finally, simulation examples are given to illustrate the effectiveness of the proposed method in this paper. (C) 2015 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.