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  Group consensus of fractional-order heterogeneous multi-agent systems with random packet losses and communication delays

Sun, F., Han, Y., Wu, X., Zhu, W., Kurths, J. (2024): Group consensus of fractional-order heterogeneous multi-agent systems with random packet losses and communication delays. - Physica A-Statistical Mechanics and its Applications, 636, 129547.
https://doi.org/10.1016/j.physa.2024.129547

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Item Permalink: https://publications.pik-potsdam.de/pubman/item/item_30770 Version Permalink: https://publications.pik-potsdam.de/pubman/item/item_30770_1
Genre: Journal Article

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sun_2024_1-s2.0-S0378437124000554-main.pdf (Publisher version), 928KB
 
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 Creators:
Sun, Fenglan1, Author              
Han, Yunpeng2, Author
Wu, Xiaoshuai2, Author
Zhu, Wei2, Author
Kurths, Jürgen1, Author              
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

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 Abstract: The group consensus problem of heterogeneous fractional-order multi-agent systems with data packet losses and communication delays is investigated in this paper, and data packet losses are described by the Bernoulli-distribution. Inspired by genetic and the infinite memory property of the Caputo fractional derivative, a novel group consensus control protocol based on sampled data is designed. Sufficient conditions for mean-square group consensus of heterogeneous fractional-order multi-agent systems are derived by using matrix theory, Gerschgorin disc theorem and graph theory. Finally, numerical simulation examples are given to verify the correctness of the theoretical results.

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Language(s): eng - English
 Dates: 2024-01-302024-02-15
 Publication Status: Finally published
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.physa.2024.129547
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Nonlinear Dynamics
Research topic keyword: Complex Networks
Model / method: Machine Learning
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Title: Physica A-Statistical Mechanics and its Applications
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 636 Sequence Number: 129547 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/1402122
Publisher: Elsevier