Iterative Learning Consensus Control for Multi-agent Systems with Fractional Order Distributed Parameter Models

Abstract

This paper concerns about the iterative learning consensus control scheme for a class of multi-agent systems (MAS) with distributed parameter models. First, based on the framework of network topologies, a second-order iterative learning control (ILC) protocol is proposed by using the nearest neighbor knowledge. Next, a discrete system for ILC is established and the consensus control problem is then converted to a stability problem for such a discrete system. Furthermore, by using generalized Gronwall inequality, a sufficient condition for the convergence of the consensus errors between any two agents is obtained. Finally, the validity of the proposed method is verified by two numerical examples.