Distributed adaptive learning consensus tracking control for a class of nonlinear 2-D multi agent systems with nonrepetitive conditions

Abstract

This study tackles the output consensus problem for a class of nonlinear two-dimensional (2-D) multi agent systems that do complex tasks repetitively in a finite domain via adaptive iterative learning control (ILC). The aim is to design a distributed adaptive learning consensus tracking control strategy that enables all 2-D multi agents to achieve the task of consensus tracking control under nonrepetitive conditions. An adaptive parameter, which adjusted by the tracking errors of the agent itself and the neighbor agents in the last iteration, is designed to approximate the unknown varying parameter of the nonlinear 2-D agent. Then, based on the approximated parameter and the iteration-varying reference surfaces, the distributed adaptive ILC strategy is obtained and the convergence of the output consensus tracking control is proved. In the end, simulations are presented to verify the effectiveness of the proposed distributed adaptive learning consensus tracking control for nonlinear 2-D multi agent system with nonrepetitive conditions.