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  Mean-square consensus for heterogeneous multi-agent systems with probabilistic time delay

Sun, F., Liao, X., Kurths, J. (2021): Mean-square consensus for heterogeneous multi-agent systems with probabilistic time delay. - Information Sciences, 543, 112-124.
https://doi.org/10.1016/j.ins.2020.07.021

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 Creators:
Sun, Fenglan1, Author              
Liao, Xiaogang2, Author
Kurths, Jürgen1, Author              
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

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 Abstract: This paper studies the delay-dependent consensus problem of heterogeneous multi-agent systems over directed topology. The heterogeneous dynamics consisting of both first-order and second-order agents with random time delay are considered. New distributed control protocols based on the probability distribution of time delay are proposed for the leader-following and leaderless systems. By adopting matrix theory, Lyapunov-Krasovskii function and stochastic analysis, some less conservative conditions for the mean-square consensus are established over directed fixed topology and switching topologies. Moreover, the larger upper bounds of time delay are obtained. Finally, several simulations are presented to illustrate the obtained results.

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 Dates: 2020-07-232021-01-08
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.ins.2020.07.021
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
MDB-ID: No data to archive
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Working Group: Network- and machine-learning-based prediction of extreme events
 Degree: -

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Title: Information Sciences
Source Genre: Journal, SCI, Scopus
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Publ. Info: -
Pages: - Volume / Issue: 543 Sequence Number: - Start / End Page: 112 - 124 Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/Information-Sciences
Publisher: Elsevier