In this paper, a distributed learning leader-follower consensus protocol based on Gaussian process regression for a class of nonlinear multi-agent systems with unknown dynamics is designed. We propose a distributed learning approach to predict the residual dynamics for each agent. The stability of the consensus protocol using the data-driven model of the dynamics is shown via Lyapunov analysis. The followers ultimately synchronize to the leader with guaranteed error bounds by applying the proposed control law with a high probability. The effectiveness and the applicability of the developed protocol are demonstrated by simulation examples.
@article{arxiv.2103.15929,
title = {Distributed Learning Consensus Control for Unknown Nonlinear Multi-Agent Systems based on Gaussian Processes},
author = {Zewen Yang and Stefan Sosnowski and Qingchen Liu and Junjie Jiao and Armin Lederer and Sandra Hirche},
journal= {arXiv preprint arXiv:2103.15929},
year = {2021}
}