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  Observer-based event-triggered formation tracking control for second-order multi-agent systems in constrained region

Sun, F., Xu, Z., Zhu, W., Kurths, J. (2025): Observer-based event-triggered formation tracking control for second-order multi-agent systems in constrained region. - Science China Information Sciences, 68, 122201.
https://doi.org/10.1007/s11432-023-4218-9

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 Urheber:
Sun, Fenglan1, Autor              
Xu, Zhonghua2, 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: In this paper, an event-triggered time-varying formation tracking control for a class of second-order nonlinear multi-agent systems (MAS) operating within a constrained region is investigated. To mitigate the negative effects of external unknown disturbance, a novel disturbance observer with performance guarantees is proposed, enabling precise disturbance estimation. Using the artificial potential field (APF) method, a repulsive potential function is introduced to prevent inter-agent collisions as well as collisions with environmental obstacles. To reduce continuous communication and frequent system updates, a sliding mode technique is incorporated into the formation tracking controller, utilizing an event-triggered mechanism. The controller is also applicable to the formation control of MAS in switching-constrained regions. The achievement of the specified time-varying geometric formation is rigorously demonstrated through the Lyapunov framework. Numerical simulations are presented to validate the effectiveness of the theoretical results.

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Sprache(n): eng - Englisch
 Datum: 2025-01-172025-01-17
 Publikationsstatus: Final veröffentlicht
 Seiten: 19
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1007/s11432-023-4218-9
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Complex Networks
Model / method: Machine Learning
 Art des Abschluß: -

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Titel: Science China Information Sciences
Genre der Quelle: Zeitschrift, SCI, Scopus
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Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 68 Artikelnummer: 122201 Start- / Endseite: - Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/1862-2836
Publisher: Springer