Learning event‐triggered control based on evolving data‐driven fuzzy granular models
This article proposes a data‐stream‐driven event‐triggered control strategy using evolving fuzzy models learned by granulation of input–output samples of nonlinear systems with unknown time‐varying dynamics. The evolving fuzzy model is obtained online from a data stream ensuring data coverage based...
Gespeichert in:
| Veröffentlicht in: | International journal of robust and nonlinear control 2022-03, Vol.32 (5), p.2805-2827 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 2827 |
|---|---|
| container_issue | 5 |
| container_start_page | 2805 |
| container_title | International journal of robust and nonlinear control |
| container_volume | 32 |
| creator | Cordovil, Luiz A. Q. Coutinho, Pedro H. S. Bessa, Iury Peixoto, Márcia L. C. Palhares, Reinaldo Martínez |
| description | This article proposes a data‐stream‐driven event‐triggered control strategy using evolving fuzzy models learned by granulation of input–output samples of nonlinear systems with unknown time‐varying dynamics. The evolving fuzzy model is obtained online from a data stream ensuring data coverage based on the principle of justifiable granularity and controlled by an event‐triggering learning mechanism dependent on the model accuracy. This evolving fuzzy model is used to design event‐triggered fuzzy controller to stabilize networked control systems while reducing the used communication resources. The event‐triggered learning mechanism is employed to determine the instants in which the event‐triggered fuzzy controller should be redesigned. Numerical examples illustrate the effectiveness of the proposed learning event‐triggered fuzzy control algorithm. |
| doi_str_mv | 10.1002/rnc.6024 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2629666160</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2629666160</sourcerecordid><originalsourceid>FETCH-LOGICAL-c2934-3d459be53075fbb1dda602f3c81e6b14c38bbb6768579c099157ebe869217b5c3</originalsourceid><addsrcrecordid>eNp10NtKwzAYB_AgCs4p-AgFb7zpzKFNm0sZnmAoiIJ3IUm_lo4umUk72a58BJ_RJzF13nqVhPy-A3-EzgmeEYzplbdmxjHNDtCEYCFSQpk4HO-ZSEtB2TE6CWGJcfyj2QS9LUB529omgQ3Y_vvzq_dt04CHKjHO9t51iVYhvpyNxHWb0VaqV5FWvo1FST3sdtuk8coOnfLJylXQhVN0VKsuwNnfOUWvtzcv8_t08XT3ML9epIYKlqWsynKhIWe4yGutSVWpuH3NTEmAa5IZVmqtecHLvBAmLk3yAjSUXFBS6NywKbrY91179z5A6OXSDd7GkZJyKjjnhOOoLvfKeBeCh1qufbtSfisJlmNuMuYmx9wiTff0o-1g-6-Tz4_zX_8DvLdxHA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2629666160</pqid></control><display><type>article</type><title>Learning event‐triggered control based on evolving data‐driven fuzzy granular models</title><source>Wiley Online Library All Journals</source><creator>Cordovil, Luiz A. Q. ; Coutinho, Pedro H. S. ; Bessa, Iury ; Peixoto, Márcia L. C. ; Palhares, Reinaldo Martínez</creator><creatorcontrib>Cordovil, Luiz A. Q. ; Coutinho, Pedro H. S. ; Bessa, Iury ; Peixoto, Márcia L. C. ; Palhares, Reinaldo Martínez</creatorcontrib><description>This article proposes a data‐stream‐driven event‐triggered control strategy using evolving fuzzy models learned by granulation of input–output samples of nonlinear systems with unknown time‐varying dynamics. The evolving fuzzy model is obtained online from a data stream ensuring data coverage based on the principle of justifiable granularity and controlled by an event‐triggering learning mechanism dependent on the model accuracy. This evolving fuzzy model is used to design event‐triggered fuzzy controller to stabilize networked control systems while reducing the used communication resources. The event‐triggered learning mechanism is employed to determine the instants in which the event‐triggered fuzzy controller should be redesigned. Numerical examples illustrate the effectiveness of the proposed learning event‐triggered fuzzy control algorithm.</description><identifier>ISSN: 1049-8923</identifier><identifier>EISSN: 1099-1239</identifier><identifier>DOI: 10.1002/rnc.6024</identifier><language>eng</language><publisher>Bognor Regis: Wiley Subscription Services, Inc</publisher><subject>Algorithms ; Control algorithms ; Control systems design ; Control theory ; Controllers ; Data transmission ; data‐driven modeling ; Event triggered control ; Evolution ; evolving fuzzy systems ; Fuzzy control ; fuzzy granular computing ; Granulation ; learning event‐triggered control ; Machine learning ; Model accuracy ; Network control ; Nonlinear systems</subject><ispartof>International journal of robust and nonlinear control, 2022-03, Vol.32 (5), p.2805-2827</ispartof><rights>2022 John Wiley & Sons Ltd.</rights><rights>2022 John Wiley & Sons, Ltd.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2934-3d459be53075fbb1dda602f3c81e6b14c38bbb6768579c099157ebe869217b5c3</citedby><cites>FETCH-LOGICAL-c2934-3d459be53075fbb1dda602f3c81e6b14c38bbb6768579c099157ebe869217b5c3</cites><orcidid>0000-0001-7369-6297 ; 0000-0001-9503-856X ; 0000-0003-2470-4240 ; 0000-0002-6603-3476 ; 0000-0002-6043-7103</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Frnc.6024$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Frnc.6024$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,27924,27925,45574,45575</link.rule.ids></links><search><creatorcontrib>Cordovil, Luiz A. Q.</creatorcontrib><creatorcontrib>Coutinho, Pedro H. S.</creatorcontrib><creatorcontrib>Bessa, Iury</creatorcontrib><creatorcontrib>Peixoto, Márcia L. C.</creatorcontrib><creatorcontrib>Palhares, Reinaldo Martínez</creatorcontrib><title>Learning event‐triggered control based on evolving data‐driven fuzzy granular models</title><title>International journal of robust and nonlinear control</title><description>This article proposes a data‐stream‐driven event‐triggered control strategy using evolving fuzzy models learned by granulation of input–output samples of nonlinear systems with unknown time‐varying dynamics. The evolving fuzzy model is obtained online from a data stream ensuring data coverage based on the principle of justifiable granularity and controlled by an event‐triggering learning mechanism dependent on the model accuracy. This evolving fuzzy model is used to design event‐triggered fuzzy controller to stabilize networked control systems while reducing the used communication resources. The event‐triggered learning mechanism is employed to determine the instants in which the event‐triggered fuzzy controller should be redesigned. Numerical examples illustrate the effectiveness of the proposed learning event‐triggered fuzzy control algorithm.</description><subject>Algorithms</subject><subject>Control algorithms</subject><subject>Control systems design</subject><subject>Control theory</subject><subject>Controllers</subject><subject>Data transmission</subject><subject>data‐driven modeling</subject><subject>Event triggered control</subject><subject>Evolution</subject><subject>evolving fuzzy systems</subject><subject>Fuzzy control</subject><subject>fuzzy granular computing</subject><subject>Granulation</subject><subject>learning event‐triggered control</subject><subject>Machine learning</subject><subject>Model accuracy</subject><subject>Network control</subject><subject>Nonlinear systems</subject><issn>1049-8923</issn><issn>1099-1239</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNp10NtKwzAYB_AgCs4p-AgFb7zpzKFNm0sZnmAoiIJ3IUm_lo4umUk72a58BJ_RJzF13nqVhPy-A3-EzgmeEYzplbdmxjHNDtCEYCFSQpk4HO-ZSEtB2TE6CWGJcfyj2QS9LUB529omgQ3Y_vvzq_dt04CHKjHO9t51iVYhvpyNxHWb0VaqV5FWvo1FST3sdtuk8coOnfLJylXQhVN0VKsuwNnfOUWvtzcv8_t08XT3ML9epIYKlqWsynKhIWe4yGutSVWpuH3NTEmAa5IZVmqtecHLvBAmLk3yAjSUXFBS6NywKbrY91179z5A6OXSDd7GkZJyKjjnhOOoLvfKeBeCh1qufbtSfisJlmNuMuYmx9wiTff0o-1g-6-Tz4_zX_8DvLdxHA</recordid><startdate>20220325</startdate><enddate>20220325</enddate><creator>Cordovil, Luiz A. Q.</creator><creator>Coutinho, Pedro H. S.</creator><creator>Bessa, Iury</creator><creator>Peixoto, Márcia L. C.</creator><creator>Palhares, Reinaldo Martínez</creator><general>Wiley Subscription Services, Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0001-7369-6297</orcidid><orcidid>https://orcid.org/0000-0001-9503-856X</orcidid><orcidid>https://orcid.org/0000-0003-2470-4240</orcidid><orcidid>https://orcid.org/0000-0002-6603-3476</orcidid><orcidid>https://orcid.org/0000-0002-6043-7103</orcidid></search><sort><creationdate>20220325</creationdate><title>Learning event‐triggered control based on evolving data‐driven fuzzy granular models</title><author>Cordovil, Luiz A. Q. ; Coutinho, Pedro H. S. ; Bessa, Iury ; Peixoto, Márcia L. C. ; Palhares, Reinaldo Martínez</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2934-3d459be53075fbb1dda602f3c81e6b14c38bbb6768579c099157ebe869217b5c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Algorithms</topic><topic>Control algorithms</topic><topic>Control systems design</topic><topic>Control theory</topic><topic>Controllers</topic><topic>Data transmission</topic><topic>data‐driven modeling</topic><topic>Event triggered control</topic><topic>Evolution</topic><topic>evolving fuzzy systems</topic><topic>Fuzzy control</topic><topic>fuzzy granular computing</topic><topic>Granulation</topic><topic>learning event‐triggered control</topic><topic>Machine learning</topic><topic>Model accuracy</topic><topic>Network control</topic><topic>Nonlinear systems</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cordovil, Luiz A. Q.</creatorcontrib><creatorcontrib>Coutinho, Pedro H. S.</creatorcontrib><creatorcontrib>Bessa, Iury</creatorcontrib><creatorcontrib>Peixoto, Márcia L. C.</creatorcontrib><creatorcontrib>Palhares, Reinaldo Martínez</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>International journal of robust and nonlinear control</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cordovil, Luiz A. Q.</au><au>Coutinho, Pedro H. S.</au><au>Bessa, Iury</au><au>Peixoto, Márcia L. C.</au><au>Palhares, Reinaldo Martínez</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Learning event‐triggered control based on evolving data‐driven fuzzy granular models</atitle><jtitle>International journal of robust and nonlinear control</jtitle><date>2022-03-25</date><risdate>2022</risdate><volume>32</volume><issue>5</issue><spage>2805</spage><epage>2827</epage><pages>2805-2827</pages><issn>1049-8923</issn><eissn>1099-1239</eissn><abstract>This article proposes a data‐stream‐driven event‐triggered control strategy using evolving fuzzy models learned by granulation of input–output samples of nonlinear systems with unknown time‐varying dynamics. The evolving fuzzy model is obtained online from a data stream ensuring data coverage based on the principle of justifiable granularity and controlled by an event‐triggering learning mechanism dependent on the model accuracy. This evolving fuzzy model is used to design event‐triggered fuzzy controller to stabilize networked control systems while reducing the used communication resources. The event‐triggered learning mechanism is employed to determine the instants in which the event‐triggered fuzzy controller should be redesigned. Numerical examples illustrate the effectiveness of the proposed learning event‐triggered fuzzy control algorithm.</abstract><cop>Bognor Regis</cop><pub>Wiley Subscription Services, Inc</pub><doi>10.1002/rnc.6024</doi><tpages>23</tpages><orcidid>https://orcid.org/0000-0001-7369-6297</orcidid><orcidid>https://orcid.org/0000-0001-9503-856X</orcidid><orcidid>https://orcid.org/0000-0003-2470-4240</orcidid><orcidid>https://orcid.org/0000-0002-6603-3476</orcidid><orcidid>https://orcid.org/0000-0002-6043-7103</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1049-8923 |
| ispartof | International journal of robust and nonlinear control, 2022-03, Vol.32 (5), p.2805-2827 |
| issn | 1049-8923 1099-1239 |
| language | eng |
| recordid | cdi_proquest_journals_2629666160 |
| source | Wiley Online Library All Journals |
| subjects | Algorithms Control algorithms Control systems design Control theory Controllers Data transmission data‐driven modeling Event triggered control Evolution evolving fuzzy systems Fuzzy control fuzzy granular computing Granulation learning event‐triggered control Machine learning Model accuracy Network control Nonlinear systems |
| title | Learning event‐triggered control based on evolving data‐driven fuzzy granular models |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-05T21%3A36%3A27IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Learning%20event%E2%80%90triggered%20control%20based%20on%20evolving%20data%E2%80%90driven%20fuzzy%20granular%20models&rft.jtitle=International%20journal%20of%20robust%20and%20nonlinear%20control&rft.au=Cordovil,%20Luiz%20A.%20Q.&rft.date=2022-03-25&rft.volume=32&rft.issue=5&rft.spage=2805&rft.epage=2827&rft.pages=2805-2827&rft.issn=1049-8923&rft.eissn=1099-1239&rft_id=info:doi/10.1002/rnc.6024&rft_dat=%3Cproquest_cross%3E2629666160%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2629666160&rft_id=info:pmid/&rfr_iscdi=true |