PI-type iterative learning consensus control for second-order hyperbolic distributed parameter models multi-agent systems

This paper considers the consensus control problem of multi-agent systems (MAS) with second-order hyperbolic distributed parameter models. Based on the framework of network topologies, a PI-type iterative learning control protocol is proposed by using the nearest neighbor knowledge. Using Gronwall inequality, a sufficient condition for the convergence of the consensus errors with respect to the iteration index is obtained. Finally, the validity of the proposed method is verified by two numerical examples.