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 secondorder 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.
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 secondorder 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.