Distributed Learning Consensus Control for Unknown Nonlinear Multi-Agent Systems based on Gaussian Processes

Zewen Yang, Stefan Sosnowski, Qingchen Liu, Junjie Jiao, Armin Lederer, Sandra Hirche

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.

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