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  Group Consensus for Heterogeneous Multiagent Systems With Time Delays Based on Frequency Domain Approach

Sun, F., Wu, X., Kurths, J., Zhu, W. (2023): Group Consensus for Heterogeneous Multiagent Systems With Time Delays Based on Frequency Domain Approach. - IEEE Transactions on Systems, Man, and Cybernetics: Systems, 53, 5, 2572-2582.
https://doi.org/10.1109/TSMC.2022.3213873

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???ViewItemFull_lblCiteItemAs???: https://publications.pik-potsdam.de/pubman/item/item_28616 ???ViewItemFull_lblCiteItemVersionAs???: https://publications.pik-potsdam.de/pubman/item/item_28616_1
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 ???ViewItemFull_lblCreators???:
Sun, Fenglan1, ???ENUM_CREATORROLE_AUTHOR???           
Wu, Xiaoshuai 2, ???ENUM_CREATORROLE_AUTHOR???
Kurths, Jürgen1, ???ENUM_CREATORROLE_AUTHOR???           
Zhu, Wei2, ???ENUM_CREATORROLE_AUTHOR???
???ViewItemFull_lblAffiliations???:
1Potsdam Institute for Climate Impact Research, ou_persistent13              
2External Organizations, ou_persistent22              

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 ???ViewItemFull_lblAbstract???: This article investigates the group consensus problem for heterogeneous multiagent systems with time delays via pinning control. Under the designed control protocol, agents in the system could be grouped arbitrarily, and agents in the same subgroup could converge to a constraint position. Meanwhile, the kinetics of agents in the same subgroup could be same or different. Both the fixed and switching topologies are considered. Based on the frequency domain method and stability theory, sufficient conditions for the system achieve group consensus are derived. Finally, several numerical examples are presented to verify the performance of the control protocol.

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???ViewItemFull_lblLanguages???: eng - English
 ???ViewItemFull_lblDates???: 2022-10-272023-05-01
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 ???ViewItemFull_lblIdentifiers???: ???ENUM_IDENTIFIERTYPE_DOI???: 10.1109/TSMC.2022.3213873
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???ENUM_IDENTIFIERTYPE_PIKDOMAIN???: RD4 - Complexity Science
???ENUM_IDENTIFIERTYPE_ORGANISATIONALK???: RD4 - Complexity Science
???ENUM_IDENTIFIERTYPE_RESEARCHTK???: Complex Networks
???ENUM_IDENTIFIERTYPE_RESEARCHTK???: Nonlinear Dynamics
???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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???ENUM_IDENTIFIERTYPE_PUBLISHER???: Institute of Electrical and Electronics Engineers (IEEE)