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  On State-Constrained Containment Control For Nonlinear Multiagent Systems Using Event-Triggered Input

Wang, X., Pang, N., Xu, Y., Huang, T., Kurths, J. (2024): On State-Constrained Containment Control For Nonlinear Multiagent Systems Using Event-Triggered Input. - IEEE Transactions on Systems, Man, and Cybernetics: Systems, 54, 4, 2530-2538.
https://doi.org/10.1109/TSMC.2023.3345365

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???ViewItemFull_lblCiteItemAs???: https://publications.pik-potsdam.de/pubman/item/item_30150 ???ViewItemFull_lblCiteItemVersionAs???: https://publications.pik-potsdam.de/pubman/item/item_30150_2
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 ???ViewItemFull_lblCreators???:
Wang, Xin1, ???ENUM_CREATORROLE_AUTHOR???
Pang, Ning1, ???ENUM_CREATORROLE_AUTHOR???
Xu, Yanwei1, ???ENUM_CREATORROLE_AUTHOR???
Huang, Tingwen1, ???ENUM_CREATORROLE_AUTHOR???
Kurths, Jürgen2, ???ENUM_CREATORROLE_AUTHOR???           
???ViewItemFull_lblAffiliations???:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 ???ViewItemFull_lblAbstract???: The neural-approximation-based adaptive nonlinear containment control issue for multiagent systems with full-state constraints is studied by invoking the backstepping approach. First, the barrier Lyapunov functions are established to deal with the state constraining issue in the multiple leaders/followers control scenarios. Then, by introducing the first-order filter, the system communication burden is substantially reduced. Moreover, the event-triggered controller is constructed by utilizing the switching-based mechanism so that the system security, control accuracy, resource consumption, and imposed state constraints are neatly balanced. We prove the output of each follower can converge to the desired hull formulated by leaders under the premise that the imposed state constraints are never violated. Besides, the considered closed-loop signals are uniformly bounded. We finally present a simulation example to show the validity of the developed approach.

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???ViewItemFull_lblLanguages???: eng - English
 ???ViewItemFull_lblDates???: 2024-01-172024-04-01
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 ???ViewItemFull_lblIdentifiers???: ???ENUM_IDENTIFIERTYPE_DOI???: 10.1109/TSMC.2023.3345365
???ENUM_IDENTIFIERTYPE_PIKDOMAIN???: RD4 - Complexity Science
???ENUM_IDENTIFIERTYPE_ORGANISATIONALK???: RD4 - Complexity Science
???ENUM_IDENTIFIERTYPE_RESEARCHTK???: Complex Networks
???ENUM_IDENTIFIERTYPE_RESEARCHTK???: Nonlinear Dynamics
???ENUM_IDENTIFIERTYPE_RESEARCHTK???: Tipping Elements
???ENUM_IDENTIFIERTYPE_MODELMETHOD???: Machine Learning
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???ViewItemFull_lblSourceTitle???: IEEE Transactions on Systems, Man, and Cybernetics: Systems
???ViewItemFull_lblSourceGenre???: ???ENUM_GENRE_JOURNAL???, SCI, Scopus
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???ViewItemFull_lblPages???: ???lbl_noEntry??? ???ViewItemFull_lblSourceVolumeIssue???: 54 (4) ???ViewItemFull_lblSourceSequenceNo???: ???lbl_noEntry??? ???ViewItemFull_lblSourceStartEndPage???: 2530 - 2538 ???ViewItemFull_lblSourceIdentifier???: ???ENUM_IDENTIFIERTYPE_CONE???: https://publications.pik-potsdam.de/cone/journals/resource/IEEE-transactions-systems-man-cybernetics
???ENUM_IDENTIFIERTYPE_PUBLISHER???: Institute of Electrical and Electronics Engineers (IEEE)