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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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 Creators:
Sun, Fenglan1, Author              
Xu, Zhonghua2, 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: 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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Language(s): eng - English
 Dates: 2025-01-172025-01-17
 Publication Status: Finally published
 Pages: 19
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: 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
 Degree: -

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Title: Science China Information Sciences
Source Genre: Journal, SCI, Scopus
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Pages: - Volume / Issue: 68 Sequence Number: 122201 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/1862-2836
Publisher: Springer