Synchronization of Heterogeneous Multi‐Agent Systems by Adaptive Iterative Learning Control

Abstract

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 uncertainties based on the local information such that the followers can synchronize their trajectories to the desired one. In comparison with the existing literature, most works assume first or second order nonlinear systems, and perfect initial conditions. In order to mitigate the identical initialization condition, the applicability of alignment condition and initial rectifying action are further explored. In addition, our developed algorithms can be applied to general high order nonlinear systems. Finally, synchronization examples of networked robotic manipulators are presented to demonstrate the effectiveness of the developed methods.