Distributed Learning Consensus Control for Unknown Nonlinear Multi-Agent Systems based on Gaussian Processes
- Resource Type
- Conference
- Authors
- Yang, Zewen; Sosnowski, Stefan; Liu, Qingchen; Jiao, Junjie; Lederer, Armin; Hirche, Sandra
- Source
- 2021 60th IEEE Conference on Decision and Control (CDC) Decision and Control (CDC), 2021 60th IEEE Conference on. :4406-4411 Dec, 2021
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Computer aided instruction
Distance learning
Conferences
Gaussian processes
Consensus control
Stability analysis
Eigenvalues and eigenfunctions
- Language
- ISSN
- 2576-2370
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.