Coordinated tracking of linear multi-agent systems with a dynamic leader: An iterative learning approach

This paper is concerned with the coordinated tracking of linear multi-agent systems with a dynamic leader. The input of the leader is time-varying and cannot be obtained by any follower agents. Distributed iterative learning controllers are developed based on the relative state information of neighboring agents. Lyapunov-Krasovskii functionals are used to show the stability properties of the closed-loop network. The benefit of the proposed controllers allow for tracking the dynamic leader over any undirected connected graphs. Besides, unknown input of the leader can be identified using distributed iterative learning laws. An extension to dynamic coupling without using global information is further studied. An application to synchronization of autopilots is provided to validate its efficacy.