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  Event-Triggered Adaptive Containment Control for Heterogeneous Stochastic Nonlinear Multiagent Systems

Wang, X., Xu, R., Huang, T., Kurths, J. (2023 online): Event-Triggered Adaptive Containment Control for Heterogeneous Stochastic Nonlinear Multiagent Systems. - IEEE Transactions on Neural Networks and Learning Systems.
https://doi.org/10.1109/TNNLS.2022.3230508

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 Urheber:
Wang, Xin 1, Autor
Xu, Rui 1, Autor
Huang, Tingwen 1, Autor
Kurths, Jürgen2, Autor              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Zusammenfassung: This article investigates the event-triggered adaptive containment control problem for a class of stochastic nonlinear multiagent systems with unmeasurable states. A stochastic system with unknown heterogeneous dynamics is established to describe the agents in a random vibration environment. Besides, the uncertain nonlinear dynamics are approximated by radial basis function neural networks (NNs), and the unmeasured states are estimated by constructing the NN-based observer. In addition, the switching-threshold-based event-triggered control method is adopted with the hope of reducing communication consumption and balancing system performance and network constraints. Moreover, we develop the novel distributed containment controller by utilizing the adaptive backstepping control strategy and the dynamic surface control (DSC) approach such that the output of each follower converges to the convex hull spanned by multiple leaders, and all signals of the closed-loop system are cooperatively semi-globally uniformly ultimately bounded in mean square. Finally, we verify the efficiency of the proposed controller by the simulation examples.

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Sprache(n): eng - Englisch
 Datum: 2023-01-03
 Publikationsstatus: Online veröffentlicht
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1109/TNNLS.2022.3230508
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: IEEE Transactions on Neural Networks and Learning Systems
Genre der Quelle: Zeitschrift, SCI, Scopus
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Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: - Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/IEEE-transactions-on-neural-networks-and-learning-systems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)