Adaptive Iterative learning control for multi-agent systems consensus tracking

This paper addresses an adaptive iterative learning control (AILC) based scheme for multi-agent systems (MAS) consensus tracking under repeatable control environment. The agent dynamics are assumed to be inherently nonlinear with unknown time-varying parameters. The underline communication among followers is fixed and undirected. The leader´s trajectory is dynamically changing, and only available to a small portion of followers. By utilizing the repetitiveness of the tracking task, the unknown time-varying parameters can be effectively estimated, and the leader´s velocity is not required in the controller. Under either resetting condition or alignment condition, perfect consensus tracking for the MAS can be achieved asymptotically in the iteration domain. A simulation example is given to demonstrate the effectiveness of the proposed algorithm.