Iterative learning consensus control with initial state learning for fractional order distributed parameter models multi‐agent systems

Abstract

This paper considers the consensus control problem of multi‐agent systems (MAS) with distributed parameter models. Based on the framework of network topologies, a second‐order PI‐type iterative learning control (ILC) protocol with initial state learning is proposed by using the nearest neighbor knowledge. A discrete system for proposed 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.