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  Data-sampled time-varying formation for singular multi-agent systems with multiple leaders

Sun, F., Yu, X., Zhu, W., Kurths, J. (2025): Data-sampled time-varying formation for singular multi-agent systems with multiple leaders. - Neural Networks, 181, 106843.
https://doi.org/10.1016/j.neunet.2024.106843

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 Urheber:
Sun, Fenglan1, Autor              
Yu, Xuemei2, Autor
Zhu, Wei2, Autor
Kurths, Jürgen1, Autor              
Affiliations:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

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 Zusammenfassung: The time-varying formation problem of singular multi-agent systems under sampled data with multiple leaders is investigated in this paper. Firstly, a data-sampled time-varying formation control protocol is proposed in the current study where the communication among followers merely occurred at sampling instants, which can save the controller communication energy significantly. Secondly, necessary and sufficient conditions for the feasibility of the formation function are provided. In addition, an approach is presented to design the formation tracking control under sampled data with multiple leaders. Finally, numerical simulations validate the efficacy of the theoretical results.

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Sprache(n): eng - Englisch
 Datum: 2024-10-312025-01-01
 Publikationsstatus: Final veröffentlicht
 Seiten: 7
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1016/j.neunet.2024.106843
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
 Art des Abschluß: -

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Titel: Neural Networks
Genre der Quelle: Zeitschrift, SCI, Scopus
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Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 181 Artikelnummer: 106843 Start- / Endseite: - Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/1879-2782
Publisher: Elsevier