Pinning-Based Neural Control for Multiagent Systems With Self-Regulation Intermediate Event-Triggered Method
A pinning-based self-regulation intermediate event-triggered (ET) funnel tracking control strategy is proposed for uncertain nonlinear multiagent systems (MASs). Based on the backstepping framework, a pinning control strategy is designed to achieve the tracking control objective, which only uses the...
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description | A pinning-based self-regulation intermediate event-triggered (ET) funnel tracking control strategy is proposed for uncertain nonlinear multiagent systems (MASs). Based on the backstepping framework, a pinning control strategy is designed to achieve the tracking control objective, which only uses the communication weight between the agents without additional feedback parameters. Moreover, by designing a self-regulation triggered condition based on the tracking error, the intermediate triggered signal is calculated to replace the continuous signal in the controller, so as to achieve the goal of discontinuous update of the controller signal, and this mechanism does not need to add additional compensation function to the controller signal. At the same time, the funnel method is adopted to restrict the error of step n and avoid the possible negative impact caused by control signal. Furthermore, the nonlinear noncontinuous faults are compensated by the disturbance observer. Then, the Lyapunov stability theorem is used to prove that all signals of the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB). Finally, some simulation results confirm the effectiveness of the proposed control scheme. |
doi_str_mv | 10.1109/TNNLS.2024.3386881 |
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Based on the backstepping framework, a pinning control strategy is designed to achieve the tracking control objective, which only uses the communication weight between the agents without additional feedback parameters. Moreover, by designing a self-regulation triggered condition based on the tracking error, the intermediate triggered signal is calculated to replace the continuous signal in the controller, so as to achieve the goal of discontinuous update of the controller signal, and this mechanism does not need to add additional compensation function to the controller signal. At the same time, the funnel method is adopted to restrict the error of step <inline-formula> <tex-math notation="LaTeX">n</tex-math> </inline-formula> and avoid the possible negative impact caused by control signal. Furthermore, the nonlinear noncontinuous faults are compensated by the disturbance observer. Then, the Lyapunov stability theorem is used to prove that all signals of the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB). Finally, some simulation results confirm the effectiveness of the proposed control scheme.</description><identifier>ISSN: 2162-237X</identifier><identifier>EISSN: 2162-2388</identifier><identifier>DOI: 10.1109/TNNLS.2024.3386881</identifier><identifier>PMID: 38648124</identifier><identifier>CODEN: ITNNAL</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Backstepping ; Event-triggered (ET) control ; Multi-agent systems ; multiagent systems (MASs) ; neural network (NN) ; nonlinear faults ; pinning method ; Simulation ; Synchronization ; Target tracking ; Topology ; Vectors</subject><ispartof>IEEE transaction on neural networks and learning systems, 2024-04, Vol.PP, p.1-11</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c324t-30aac73d12b715870a1a3fcb6cb65a7086eaafa3e0bd5696af83ce259ee04c123</citedby><orcidid>0000-0003-1480-1872 ; 0000-0002-2524-4533 ; 0000-0002-7590-7411 ; 0000-0002-9519-4084</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10506210$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/10506210$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38648124$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ren, Hongru</creatorcontrib><creatorcontrib>Liu, Zeyi</creatorcontrib><creatorcontrib>Liang, Hongjing</creatorcontrib><creatorcontrib>Li, Hongyi</creatorcontrib><title>Pinning-Based Neural Control for Multiagent Systems With Self-Regulation Intermediate Event-Triggered Method</title><title>IEEE transaction on neural networks and learning systems</title><addtitle>TNNLS</addtitle><addtitle>IEEE Trans Neural Netw Learn Syst</addtitle><description>A pinning-based self-regulation intermediate event-triggered (ET) funnel tracking control strategy is proposed for uncertain nonlinear multiagent systems (MASs). Based on the backstepping framework, a pinning control strategy is designed to achieve the tracking control objective, which only uses the communication weight between the agents without additional feedback parameters. Moreover, by designing a self-regulation triggered condition based on the tracking error, the intermediate triggered signal is calculated to replace the continuous signal in the controller, so as to achieve the goal of discontinuous update of the controller signal, and this mechanism does not need to add additional compensation function to the controller signal. At the same time, the funnel method is adopted to restrict the error of step <inline-formula> <tex-math notation="LaTeX">n</tex-math> </inline-formula> and avoid the possible negative impact caused by control signal. Furthermore, the nonlinear noncontinuous faults are compensated by the disturbance observer. Then, the Lyapunov stability theorem is used to prove that all signals of the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB). Finally, some simulation results confirm the effectiveness of the proposed control scheme.</description><subject>Backstepping</subject><subject>Event-triggered (ET) control</subject><subject>Multi-agent systems</subject><subject>multiagent systems (MASs)</subject><subject>neural network (NN)</subject><subject>nonlinear faults</subject><subject>pinning method</subject><subject>Simulation</subject><subject>Synchronization</subject><subject>Target tracking</subject><subject>Topology</subject><subject>Vectors</subject><issn>2162-237X</issn><issn>2162-2388</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpNkF1LwzAUhoMoKro_ICK59KYzH22aXerwC-YUN9G7ctae1kjWapIK-_dGN8VD4OTieV84DyFHnA05Z6Oz-XQ6mQ0FE-lQSq205ltkX3AlEiG13v775y97ZOD9G4ujWKbS0S7Zi4FUc5HuE_tg2ta0TXIBHis6xd6BpeOuDa6ztO4cvettMNBgG-hs5QMuPX024ZXO0NbJIza9hWC6lt62Ad0SKwMB6eVn5JO5M02DLvbeYXjtqkOyU4P1ONjsA_J0dTkf3yST--vb8fkkKaVIQyIZQJnLiotFzjOdM-Ag63Kh4ssgZ1ohQA0S2aLK1EhBrWWJIhshsrTkQh6Q03Xvu-s-evShWBpforXQYtf7QrI04zxLtY6oWKOl67x3WBfvzizBrQrOim_RxY_o4lt0sREdQyeb_n4RL_6L_GqNwPEaMIj4rzFjSnAmvwBT-oO6</recordid><startdate>20240422</startdate><enddate>20240422</enddate><creator>Ren, Hongru</creator><creator>Liu, Zeyi</creator><creator>Liang, Hongjing</creator><creator>Li, Hongyi</creator><general>IEEE</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-1480-1872</orcidid><orcidid>https://orcid.org/0000-0002-2524-4533</orcidid><orcidid>https://orcid.org/0000-0002-7590-7411</orcidid><orcidid>https://orcid.org/0000-0002-9519-4084</orcidid></search><sort><creationdate>20240422</creationdate><title>Pinning-Based Neural Control for Multiagent Systems With Self-Regulation Intermediate Event-Triggered Method</title><author>Ren, Hongru ; Liu, Zeyi ; Liang, Hongjing ; Li, Hongyi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c324t-30aac73d12b715870a1a3fcb6cb65a7086eaafa3e0bd5696af83ce259ee04c123</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Backstepping</topic><topic>Event-triggered (ET) control</topic><topic>Multi-agent systems</topic><topic>multiagent systems (MASs)</topic><topic>neural network (NN)</topic><topic>nonlinear faults</topic><topic>pinning method</topic><topic>Simulation</topic><topic>Synchronization</topic><topic>Target tracking</topic><topic>Topology</topic><topic>Vectors</topic><toplevel>online_resources</toplevel><creatorcontrib>Ren, Hongru</creatorcontrib><creatorcontrib>Liu, Zeyi</creatorcontrib><creatorcontrib>Liang, Hongjing</creatorcontrib><creatorcontrib>Li, Hongyi</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998–Present</collection><collection>IEEE Xplore</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>IEEE transaction on neural networks and learning systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Ren, Hongru</au><au>Liu, Zeyi</au><au>Liang, Hongjing</au><au>Li, Hongyi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Pinning-Based Neural Control for Multiagent Systems With Self-Regulation Intermediate Event-Triggered Method</atitle><jtitle>IEEE transaction on neural networks and learning systems</jtitle><stitle>TNNLS</stitle><addtitle>IEEE Trans Neural Netw Learn Syst</addtitle><date>2024-04-22</date><risdate>2024</risdate><volume>PP</volume><spage>1</spage><epage>11</epage><pages>1-11</pages><issn>2162-237X</issn><eissn>2162-2388</eissn><coden>ITNNAL</coden><abstract>A pinning-based self-regulation intermediate event-triggered (ET) funnel tracking control strategy is proposed for uncertain nonlinear multiagent systems (MASs). Based on the backstepping framework, a pinning control strategy is designed to achieve the tracking control objective, which only uses the communication weight between the agents without additional feedback parameters. Moreover, by designing a self-regulation triggered condition based on the tracking error, the intermediate triggered signal is calculated to replace the continuous signal in the controller, so as to achieve the goal of discontinuous update of the controller signal, and this mechanism does not need to add additional compensation function to the controller signal. At the same time, the funnel method is adopted to restrict the error of step <inline-formula> <tex-math notation="LaTeX">n</tex-math> </inline-formula> and avoid the possible negative impact caused by control signal. Furthermore, the nonlinear noncontinuous faults are compensated by the disturbance observer. Then, the Lyapunov stability theorem is used to prove that all signals of the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB). Finally, some simulation results confirm the effectiveness of the proposed control scheme.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>38648124</pmid><doi>10.1109/TNNLS.2024.3386881</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0003-1480-1872</orcidid><orcidid>https://orcid.org/0000-0002-2524-4533</orcidid><orcidid>https://orcid.org/0000-0002-7590-7411</orcidid><orcidid>https://orcid.org/0000-0002-9519-4084</orcidid></addata></record> |
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subjects | Backstepping Event-triggered (ET) control Multi-agent systems multiagent systems (MASs) neural network (NN) nonlinear faults pinning method Simulation Synchronization Target tracking Topology Vectors |
title | Pinning-Based Neural Control for Multiagent Systems With Self-Regulation Intermediate Event-Triggered Method |
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