Event-Triggered Bipartite Consensus for Fuzzy Multiagent Systems Under Markovian Switching Signed Topology

Jiafeng Yu; Choon Ki Ahn; Peng Shi

doi:10.1109/TFUZZ.2021.3089740

Event-Triggered Bipartite Consensus for Fuzzy Multiagent Systems Under Markovian Switching Signed Topology

Jiafeng Yu, Choon Ki Ahn, Peng Shi

Research output: Contribution to journal › Article › peer-review

26 Citations (Scopus)

Abstract

In this article, we study the problems of bipartite and cooperative consensus with a strictly dissipative performance for fuzzy multiagent systems (MASs) in a unified framework. First, we prove that bipartite consensus over a structurally balanced signed graph is equivalent to cooperative consensus over the corresponding unsigned graph by leveraging the gauge transformation for a class of nonlinear MASs. Then, a polynomial fuzzy model is constructed to describe the nonlinear MAS formed by one leader and followers. For mitigating communication and computational load, a mode-dependent event-triggered transmission strategy is proposed. By establishing the switching topologies through Markovian process, a new sampled-data event-triggered consensus protocol is designed. With a mode-dependent Lyapunov-Krasovskii function, a novel relaxed dissipative criterion is obtained. The criterion guarantees that all agents can achieve both event-triggered cooperative consensus and event-triggered bipartite consensus with the same magnitude but opposite signs for MASs over structurally balanced signed directed graphs and Markovian switching topologies. Moreover, the event-triggered parameters and consensus control gains can be numerically solved via the sum-of-squares method. Simulation results are given to show the effectiveness of the proposed design method.

Original language	English
Pages (from-to)	2610-2620
Number of pages	11
Journal	IEEE Transactions on Fuzzy Systems
Volume	30
Issue number	7
DOIs	https://doi.org/10.1109/TFUZZ.2021.3089740
Publication status	Published - 2022 Jul 1

Bibliographical note

Funding Information:
This work was supported in part by the Jiangsu Province Natural Science Foundation under GrantBK20191457, in part by theKey-areaResearch andDevelopment Program of Guangdong Province under Grant 2020B0909020001, in part by the Science and Technology Research Project of Chongqing Education Commission under Grant KJZD-M201900801 and Grant KJQN201900831, in part by Chongqing Natural Science Foundation under Grant cstc2020jcyj-msxmX0077, and in part by the National Research Foundation of Korea (NRF) funded by the Korea government (Ministry of Science and ICT) under Grant NRF-2020R1A2C1005449.

Publisher Copyright:
© 1993-2012 IEEE.

Keywords

Event-triggered control (ETC)
Markovian switching topology
fuzzy modeling
multiagent system (MAS)
strict dissipativity

ASJC Scopus subject areas

Control and Systems Engineering
Computational Theory and Mathematics
Artificial Intelligence
Applied Mathematics

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10.1109/TFUZZ.2021.3089740

Cite this

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title = "Event-Triggered Bipartite Consensus for Fuzzy Multiagent Systems Under Markovian Switching Signed Topology",

abstract = "In this article, we study the problems of bipartite and cooperative consensus with a strictly dissipative performance for fuzzy multiagent systems (MASs) in a unified framework. First, we prove that bipartite consensus over a structurally balanced signed graph is equivalent to cooperative consensus over the corresponding unsigned graph by leveraging the gauge transformation for a class of nonlinear MASs. Then, a polynomial fuzzy model is constructed to describe the nonlinear MAS formed by one leader and followers. For mitigating communication and computational load, a mode-dependent event-triggered transmission strategy is proposed. By establishing the switching topologies through Markovian process, a new sampled-data event-triggered consensus protocol is designed. With a mode-dependent Lyapunov-Krasovskii function, a novel relaxed dissipative criterion is obtained. The criterion guarantees that all agents can achieve both event-triggered cooperative consensus and event-triggered bipartite consensus with the same magnitude but opposite signs for MASs over structurally balanced signed directed graphs and Markovian switching topologies. Moreover, the event-triggered parameters and consensus control gains can be numerically solved via the sum-of-squares method. Simulation results are given to show the effectiveness of the proposed design method.",

keywords = "Event-triggered control (ETC), Markovian switching topology, fuzzy modeling, multiagent system (MAS), strict dissipativity",

author = "Jiafeng Yu and Ahn, {Choon Ki} and Peng Shi",

note = "Funding Information: This work was supported in part by the Jiangsu Province Natural Science Foundation under GrantBK20191457, in part by theKey-areaResearch andDevelopment Program of Guangdong Province under Grant 2020B0909020001, in part by the Science and Technology Research Project of Chongqing Education Commission under Grant KJZD-M201900801 and Grant KJQN201900831, in part by Chongqing Natural Science Foundation under Grant cstc2020jcyj-msxmX0077, and in part by the National Research Foundation of Korea (NRF) funded by the Korea government (Ministry of Science and ICT) under Grant NRF-2020R1A2C1005449. Publisher Copyright: {\textcopyright} 1993-2012 IEEE.",

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N2 - In this article, we study the problems of bipartite and cooperative consensus with a strictly dissipative performance for fuzzy multiagent systems (MASs) in a unified framework. First, we prove that bipartite consensus over a structurally balanced signed graph is equivalent to cooperative consensus over the corresponding unsigned graph by leveraging the gauge transformation for a class of nonlinear MASs. Then, a polynomial fuzzy model is constructed to describe the nonlinear MAS formed by one leader and followers. For mitigating communication and computational load, a mode-dependent event-triggered transmission strategy is proposed. By establishing the switching topologies through Markovian process, a new sampled-data event-triggered consensus protocol is designed. With a mode-dependent Lyapunov-Krasovskii function, a novel relaxed dissipative criterion is obtained. The criterion guarantees that all agents can achieve both event-triggered cooperative consensus and event-triggered bipartite consensus with the same magnitude but opposite signs for MASs over structurally balanced signed directed graphs and Markovian switching topologies. Moreover, the event-triggered parameters and consensus control gains can be numerically solved via the sum-of-squares method. Simulation results are given to show the effectiveness of the proposed design method.

AB - In this article, we study the problems of bipartite and cooperative consensus with a strictly dissipative performance for fuzzy multiagent systems (MASs) in a unified framework. First, we prove that bipartite consensus over a structurally balanced signed graph is equivalent to cooperative consensus over the corresponding unsigned graph by leveraging the gauge transformation for a class of nonlinear MASs. Then, a polynomial fuzzy model is constructed to describe the nonlinear MAS formed by one leader and followers. For mitigating communication and computational load, a mode-dependent event-triggered transmission strategy is proposed. By establishing the switching topologies through Markovian process, a new sampled-data event-triggered consensus protocol is designed. With a mode-dependent Lyapunov-Krasovskii function, a novel relaxed dissipative criterion is obtained. The criterion guarantees that all agents can achieve both event-triggered cooperative consensus and event-triggered bipartite consensus with the same magnitude but opposite signs for MASs over structurally balanced signed directed graphs and Markovian switching topologies. Moreover, the event-triggered parameters and consensus control gains can be numerically solved via the sum-of-squares method. Simulation results are given to show the effectiveness of the proposed design method.

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Event-Triggered Bipartite Consensus for Fuzzy Multiagent Systems Under Markovian Switching Signed Topology

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Keywords

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