Adaptive Learning Control for Second-Order Nonlinear Multi-Agent Systems with Iteration-Switching Topologies

Abstract

This paper investigates the consensus tracking problem for the leader-follower second-order uncertain nonlinear multi-agent systems with nonrepeatable mismatched input disturbance. The main structure of the proposed adaptive iterative learning controller contains a neural networks learning component and a robust learning component. The effect of the neural networks learning component is to estimate the system’s nonlinearity and the robust learning component is to suppress the nonlinear input gain and disturbance. An adaptive law combining time- and iteration- domain is used to tune the controller parameters. We use the composite energy function method to prove the consensus convergence and give a numerical simulation to illustrate the effectiveness of the proposed scheme.