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  Consensus seeking in multiagent systems with Markovian switching topology under aperiodic sampled data

Wang, X., Wang, H., Li, C., Huang, T., Kurths, J. (2020): Consensus seeking in multiagent systems with Markovian switching topology under aperiodic sampled data. - IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50, 12, 5189-5200.
https://doi.org/10.1109/TSMC.2018.2867900

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 Urheber:
Wang, Xin1, Autor              
Wang, Hui2, Autor
Li, Chuandong2, Autor
Huang, Tingwen2, Autor
Kurths, Jürgen1, Autor              
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

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 Zusammenfassung: This paper is concerned with the consensus issue for a class of multiagent systems with Markovian switching topology under aperiodic sampled data measurements. By constructing a novel piecewise stochastic Lyapunov-Krasovskii functional, some novel conditions with less conservative are established such that the consensus is achieved in the mean square sense. In contrast to some previous publications, the sample period is no longer fixed and the transition probability matrix of Markovian switching topology is uncertain. This issue which is of practical and theoretical significance is further investigated when the sampled data controller of each agent is suffered from distinct time-varying input delay. Quite different with the related studies, a maximally allowable input delay upper bound is replaced by the permissible input delay interval. Furthermore, the corresponding consensus is elegantly obtained in terms of linear matrix inequalities. Finally, the effectiveness and practicability of our consensus criteria are well illustrated by the numerical examples.

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 Datum: 2018-09-262020-12
 Publikationsstatus: Final veröffentlicht
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1109/TSMC.2018.2867900
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
MDB-ID: No data to archive
Model / method: Machine Learning
Research topic keyword: Complex Networks
Research topic keyword: Nonlinear Dynamics
Working Group: Network- and machine-learning-based prediction of extreme events
 Art des Abschluß: -

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Titel: IEEE Transactions on Systems, Man, and Cybernetics: Systems
Genre der Quelle: Zeitschrift, SCI, Scopus
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Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 50 (12) Artikelnummer: - Start- / Endseite: 5189 - 5200 Identifikator: Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Anderer: 2168-2216
CoNE: https://publications.pik-potsdam.de/cone/journals/resource/IEEE-transactions-systems-man-cybernetics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)