Learning nonlinear dynamics in synchronization of knowledge-based leader-following networks

Shimin WANG; Xiangyu MENG; Hongwei ZHANG; Frank L. LEWIS

doi:10.1016/j.automatica.2024.111695

Learning nonlinear dynamics in synchronization of knowledge-based leader-following networks

Shimin WANG
, Xiangyu MENG
, Hongwei ZHANG^*
, Frank L. LEWIS

^*Corresponding author for this work

Research output: Journal Publications › Journal Article (refereed) › peer-review

3 Citations (Scopus)

Abstract

Knowledge-based leader-following synchronization of heterogeneous nonlinear multi-agent systems is a challenging problem since the leader's dynamic information is unknown to any follower node. This paper proposes a learning-based fully distributed observer for a class of nonlinear leader systems, which can simultaneously learn the leader's dynamics and states. This class of leader dynamics is rather general and does not require a bounded Jacobian matrix. Based on this learning-based distributed observer, we further synthesize an adaptive distributed control law for solving the leader-following synchronization problem of multiple Euler–Lagrange systems subject to an uncertain nonlinear leader system. The results are illustrated by a simulation example.

Original language	English
Article number	111695
Number of pages	9
Journal	Automatica
Volume	166
Early online date	4 May 2024
DOIs	https://doi.org/10.1016/j.automatica.2024.111695
Publication status	Published - Aug 2024
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2024 Elsevier Ltd

Funding

This work was supported by the Guangdong Basic and Applied Basic Research Foundation under project 2023A1515011981, Shenzhen Science and Technology Program under project GXWD20231129102406001, National Natural Science Foundation of China under project 62073158, and the NSERC, Canada.

Keywords

Distributed observer
Euler–Lagrange system
Multi-agent system
Parameter estimation
Synchronization

Access to Document

10.1016/j.automatica.2024.111695

Cite this

@article{1dc2bba3fb334b6c92c58644b11ea2f2,

title = "Learning nonlinear dynamics in synchronization of knowledge-based leader-following networks",

abstract = "Knowledge-based leader-following synchronization of heterogeneous nonlinear multi-agent systems is a challenging problem since the leader's dynamic information is unknown to any follower node. This paper proposes a learning-based fully distributed observer for a class of nonlinear leader systems, which can simultaneously learn the leader's dynamics and states. This class of leader dynamics is rather general and does not require a bounded Jacobian matrix. Based on this learning-based distributed observer, we further synthesize an adaptive distributed control law for solving the leader-following synchronization problem of multiple Euler–Lagrange systems subject to an uncertain nonlinear leader system. The results are illustrated by a simulation example.",

keywords = "Distributed observer, Euler–Lagrange system, Multi-agent system, Parameter estimation, Synchronization",

author = "Shimin WANG and Xiangyu MENG and Hongwei ZHANG and LEWIS, \{Frank L.\}",

note = "Publisher Copyright: {\textcopyright} 2024 Elsevier Ltd",

year = "2024",

month = aug,

doi = "10.1016/j.automatica.2024.111695",

language = "English",

volume = "166",

journal = "Automatica",

issn = "0005-1098",

publisher = "Elsevier Ltd",

}

TY - JOUR

T1 - Learning nonlinear dynamics in synchronization of knowledge-based leader-following networks

AU - WANG, Shimin

AU - MENG, Xiangyu

AU - ZHANG, Hongwei

AU - LEWIS, Frank L.

PY - 2024/8

Y1 - 2024/8

N2 - Knowledge-based leader-following synchronization of heterogeneous nonlinear multi-agent systems is a challenging problem since the leader's dynamic information is unknown to any follower node. This paper proposes a learning-based fully distributed observer for a class of nonlinear leader systems, which can simultaneously learn the leader's dynamics and states. This class of leader dynamics is rather general and does not require a bounded Jacobian matrix. Based on this learning-based distributed observer, we further synthesize an adaptive distributed control law for solving the leader-following synchronization problem of multiple Euler–Lagrange systems subject to an uncertain nonlinear leader system. The results are illustrated by a simulation example.

AB - Knowledge-based leader-following synchronization of heterogeneous nonlinear multi-agent systems is a challenging problem since the leader's dynamic information is unknown to any follower node. This paper proposes a learning-based fully distributed observer for a class of nonlinear leader systems, which can simultaneously learn the leader's dynamics and states. This class of leader dynamics is rather general and does not require a bounded Jacobian matrix. Based on this learning-based distributed observer, we further synthesize an adaptive distributed control law for solving the leader-following synchronization problem of multiple Euler–Lagrange systems subject to an uncertain nonlinear leader system. The results are illustrated by a simulation example.

KW - Distributed observer

KW - Euler–Lagrange system

KW - Multi-agent system

KW - Parameter estimation

KW - Synchronization

UR - https://www.scopus.com/pages/publications/85192015105

U2 - 10.1016/j.automatica.2024.111695

DO - 10.1016/j.automatica.2024.111695

M3 - Journal Article (refereed)

AN - SCOPUS:85192015105

SN - 0005-1098

VL - 166

JO - Automatica

JF - Automatica

M1 - 111695

ER -

Learning nonlinear dynamics in synchronization of knowledge-based leader-following networks

Abstract

Bibliographical note

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Cite this