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  Group consensus of heterogeneous multi-agent systems with packet loss and unknown speed of second-order agents in cooperative–competitive networks

Sun, F., Wu, X., Kurths, J., Zhu, W. (2022): Group consensus of heterogeneous multi-agent systems with packet loss and unknown speed of second-order agents in cooperative–competitive networks. - Nonlinear Dynamics, 110, 3447-3461.
https://doi.org/10.1007/s11071-022-07780-1

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Item Permalink: https://publications.pik-potsdam.de/pubman/item/item_27967 Version Permalink: https://publications.pik-potsdam.de/pubman/item/item_27967_2
Genre: Journal Article

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sun_2022_s11071-022-07780-1.pdf (Publisher version), 949KB
 
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 Creators:
Sun, Fenglan1, Author              
Wu, Xiaoshuai2, Author
Kurths, Jürgen1, Author              
Zhu, Wei2, Author
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

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 Abstract: The group consensus problem is investigated for discrete-time heterogeneous multi-agent systems with unknown speed of second-order agents in cooperative–competitive networks. Time delays and data packet loss are considered in the systems, and novel group consensus control protocols are designed. By using graph theory, matrix theory, and the frequency domain approach, some sufficient conditions for group consensus are obtained, and the upper bound of the allowed time delay in the consensus system is given. Finally, a series of simulation experiments are presented to verify the effectiveness of the propounded control protocol.

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Language(s): eng - English
 Dates: 2022-09-062022-12
 Publication Status: Finally published
 Pages: 15
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1007/s11071-022-07780-1
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
Working Group: Network- and machine-learning-based prediction of extreme events
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Title: Nonlinear Dynamics
Source Genre: Journal, SCI, Scopus
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Pages: - Volume / Issue: 110 Sequence Number: - Start / End Page: 3447 - 3461 Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/nonlinear-dynamics
Publisher: Springer