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

doi:10.1109/CDC45484.2021.9683522

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

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Publikation: Beitrag in Buch/Bericht/Konferenzband › Konferenzbeitrag › Begutachtung

10 Zitate (Scopus)

Abstract

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.

Originalsprache	Englisch
Titel	60th IEEE Conference on Decision and Control, CDC 2021
Herausgeber (Verlag)	Institute of Electrical and Electronics Engineers Inc.
Seiten	4406-4411
Seitenumfang	6
ISBN (elektronisch)	9781665436595
DOIs	https://doi.org/10.1109/CDC45484.2021.9683522
Publikationsstatus	Veröffentlicht - 2021
Veranstaltung	60th IEEE Conference on Decision and Control, CDC 2021 - Austin, USA/Vereinigte Staaten Dauer: 13 Dez. 2021 → 17 Dez. 2021

Publikationsreihe

Name	Proceedings of the IEEE Conference on Decision and Control
Band	2021-December
ISSN (Print)	0743-1546
ISSN (elektronisch)	2576-2370

Konferenz

Konferenz	60th IEEE Conference on Decision and Control, CDC 2021
Land/Gebiet	USA/Vereinigte Staaten
Ort	Austin
Zeitraum	13/12/21 → 17/12/21

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10.1109/CDC45484.2021.9683522

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Yang, Z., Sosnowski, S., Liu, Q., Jiao, J., Lederer, A., & Hirche, S. (2021). Distributed Learning Consensus Control for Unknown Nonlinear Multi-Agent Systems based on Gaussian Processes. in 60th IEEE Conference on Decision and Control, CDC 2021 (S. 4406-4411). (Proceedings of the IEEE Conference on Decision and Control; Band 2021-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC45484.2021.9683522

@inproceedings{7f6b54eb2fdd42629a472295c97dc7e0,

title = "Distributed Learning Consensus Control for Unknown Nonlinear Multi-Agent Systems based on Gaussian Processes",

abstract = "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.",

author = "Zewen Yang and Stefan Sosnowski and Qingchen Liu and Junjie Jiao and Armin Lederer and Sandra Hirche",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 60th IEEE Conference on Decision and Control, CDC 2021 ; Conference date: 13-12-2021 Through 17-12-2021",

year = "2021",

doi = "10.1109/CDC45484.2021.9683522",

language = "English",

series = "Proceedings of the IEEE Conference on Decision and Control",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "4406--4411",

booktitle = "60th IEEE Conference on Decision and Control, CDC 2021",

}

Yang, Z, Sosnowski, S, Liu, Q, Jiao, J, Lederer, A & Hirche, S 2021, Distributed Learning Consensus Control for Unknown Nonlinear Multi-Agent Systems based on Gaussian Processes. in 60th IEEE Conference on Decision and Control, CDC 2021. Proceedings of the IEEE Conference on Decision and Control, Bd. 2021-December, Institute of Electrical and Electronics Engineers Inc., S. 4406-4411, 60th IEEE Conference on Decision and Control, CDC 2021, Austin, USA/Vereinigte Staaten, 13/12/21. https://doi.org/10.1109/CDC45484.2021.9683522

Distributed Learning Consensus Control for Unknown Nonlinear Multi-Agent Systems based on Gaussian Processes. / Yang, Zewen; Sosnowski, Stefan; Liu, Qingchen et al.
60th IEEE Conference on Decision and Control, CDC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. S. 4406-4411 (Proceedings of the IEEE Conference on Decision and Control; Band 2021-December).

Publikation: Beitrag in Buch/Bericht/Konferenzband › Konferenzbeitrag › Begutachtung

TY - GEN

T1 - Distributed Learning Consensus Control for Unknown Nonlinear Multi-Agent Systems based on Gaussian Processes

AU - Yang, Zewen

AU - Sosnowski, Stefan

AU - Liu, Qingchen

AU - Jiao, Junjie

AU - Lederer, Armin

AU - Hirche, Sandra

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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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U2 - 10.1109/CDC45484.2021.9683522

DO - 10.1109/CDC45484.2021.9683522

M3 - Conference contribution

AN - SCOPUS:85125995624

T3 - Proceedings of the IEEE Conference on Decision and Control

SP - 4406

EP - 4411

BT - 60th IEEE Conference on Decision and Control, CDC 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 60th IEEE Conference on Decision and Control, CDC 2021

Y2 - 13 December 2021 through 17 December 2021

ER -

Yang Z, Sosnowski S, Liu Q, Jiao J, Lederer A, Hirche S. Distributed Learning Consensus Control for Unknown Nonlinear Multi-Agent Systems based on Gaussian Processes. in 60th IEEE Conference on Decision and Control, CDC 2021. Institute of Electrical and Electronics Engineers Inc. 2021. S. 4406-4411. (Proceedings of the IEEE Conference on Decision and Control). doi: 10.1109/CDC45484.2021.9683522

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

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