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  Mean-square consensus of hybrid multi-agent systems with noise and nonlinear terms over jointly connected topologies

Sun, F., Lu, C., Zhu, W., Kurths, J. (2023): Mean-square consensus of hybrid multi-agent systems with noise and nonlinear terms over jointly connected topologies. - Journal of the Franklin Institute, 360, 8, 5759-5779.
https://doi.org/10.1016/j.jfranklin.2023.03.031

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 Creators:
Sun, Fenglan1, Author
Lu, Chuan1, Author
Zhu, Wei1, Author
Kurths, Jürgen2, Author              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: This paper studies the mean-square consensus of second-order hybrid multi-agent systems over jointly connected topologies. Systems with time-varying delay and multiplicative noise are considered. The date sampling control technique is adopted. Through matrix transformation, a positive definite matrix transformed by the Laplacian matrix is obtained, where the Laplacian matrix is a connected subgraph divided by the jointly connected topologies. By using graph theory, matrix theory and Lyapunov stability theory, sufficient conditions and the upper bound of time delays for the mean-square consensus are obtained. Finally, several simulations are presented to demonstrate the validity of the control method.

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Language(s): eng - English
 Dates: 2023-04-192023-05
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.jfranklin.2023.03.031
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Model / method: Machine Learning
 Degree: -

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Title: Journal of the Franklin Institute
Source Genre: Journal, SCI, Scopus
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Pages: - Volume / Issue: 360 (8) Sequence Number: - Start / End Page: 5759 - 5779 Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journal-franklin-institute
Publisher: Elsevier