Synchronization of heterogeneous multi-agent systems by adaptive iterative learning control

Abstract

In this work, under a repeatable control environment, an adaptive iterative learning control method is applied to synchronize a group of uncertain heterogeneous agents. The agent dynamics are modeled by nonlinear equations, which contain both parametric and non-parametric uncertainties. Furthermore, the uncertainties are assumed to be general nonlinear terms instead of the global Lipschitz functions. The communication among the followers is depicted by an undirected and connected graph, meanwhile, the virtual leader's trajectory is only accessible to a small portion of the followers. The proposed learning rules enable all the followers to learn and handle both parametric and non-parametric u..