Adaptive full order sliding mode control for electronic throttle valve system with fixed time convergence using extreme learning machine
This paper proposes a novel extreme learning machine (ELM)-based fixed time adaptive trajectory control for electronic throttle valve system with uncertain dynamics and external disturbances. The developed control strategy consists of a recursive full order terminal sliding mode structure based on t...
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| Veröffentlicht in: | Neural computing & applications 2022-04, Vol.34 (7), p.5241-5253 |
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| container_title | Neural computing & applications |
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| creator | Hu, Youhao Wang, Hai Yazdani, Amirmehdi Man, Zhihong |
| description | This paper proposes a novel extreme learning machine (ELM)-based fixed time adaptive trajectory control for electronic throttle valve system with uncertain dynamics and external disturbances. The developed control strategy consists of a recursive full order terminal sliding mode structure based on the bilimit homogeneous property and a lumped uncertainty changing rate upper bound estimator via an adaptive ELM algorithm such that not only the fixed time convergence for both sliding variable and error states can be guaranteed, but also the chattering phenomenon can be suppressed effectively. The stability of the closed-loop system is proved rigorously based on Lyapunov theory. The simulation results are given to verify the superior tracking performance of the proposed control strategy. |
| doi_str_mv | 10.1007/s00521-021-06365-0 |
| format | Article |
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The developed control strategy consists of a recursive full order terminal sliding mode structure based on the bilimit homogeneous property and a lumped uncertainty changing rate upper bound estimator via an adaptive ELM algorithm such that not only the fixed time convergence for both sliding variable and error states can be guaranteed, but also the chattering phenomenon can be suppressed effectively. The stability of the closed-loop system is proved rigorously based on Lyapunov theory. The simulation results are given to verify the superior tracking performance of the proposed control strategy.</description><identifier>ISSN: 0941-0643</identifier><identifier>EISSN: 1433-3058</identifier><identifier>DOI: 10.1007/s00521-021-06365-0</identifier><language>eng</language><publisher>London: Springer London</publisher><subject>Adaptive algorithms ; Adaptive control ; Artificial Intelligence ; Artificial neural networks ; Closed loop systems ; Computational Biology/Bioinformatics ; Computational Science and Engineering ; Computer Science ; Control valves ; Convergence ; Data Mining and Knowledge Discovery ; Feedback control ; Image Processing and Computer Vision ; Machine learning ; Neural networks ; Probability and Statistics in Computer Science ; S.I: Computational Intelligence-based Control and Estimation in Mechatronic Systems ; Sliding mode control ; Special Issue on Computational Intelligence-based Control and Estimation in Mechatronic Systems ; Trajectory control ; Upper bounds</subject><ispartof>Neural computing & applications, 2022-04, Vol.34 (7), p.5241-5253</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021</rights><rights>The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-f494a3ea36b89f922d8d39bde7540d198e6c7acee2f77defeb46a02b81d8055f3</citedby><cites>FETCH-LOGICAL-c319t-f494a3ea36b89f922d8d39bde7540d198e6c7acee2f77defeb46a02b81d8055f3</cites><orcidid>0000-0003-2789-9530</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00521-021-06365-0$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00521-021-06365-0$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Hu, Youhao</creatorcontrib><creatorcontrib>Wang, Hai</creatorcontrib><creatorcontrib>Yazdani, Amirmehdi</creatorcontrib><creatorcontrib>Man, Zhihong</creatorcontrib><title>Adaptive full order sliding mode control for electronic throttle valve system with fixed time convergence using extreme learning machine</title><title>Neural computing & applications</title><addtitle>Neural Comput & Applic</addtitle><description>This paper proposes a novel extreme learning machine (ELM)-based fixed time adaptive trajectory control for electronic throttle valve system with uncertain dynamics and external disturbances. The developed control strategy consists of a recursive full order terminal sliding mode structure based on the bilimit homogeneous property and a lumped uncertainty changing rate upper bound estimator via an adaptive ELM algorithm such that not only the fixed time convergence for both sliding variable and error states can be guaranteed, but also the chattering phenomenon can be suppressed effectively. The stability of the closed-loop system is proved rigorously based on Lyapunov theory. The simulation results are given to verify the superior tracking performance of the proposed control strategy.</description><subject>Adaptive algorithms</subject><subject>Adaptive control</subject><subject>Artificial Intelligence</subject><subject>Artificial neural networks</subject><subject>Closed loop systems</subject><subject>Computational Biology/Bioinformatics</subject><subject>Computational Science and Engineering</subject><subject>Computer Science</subject><subject>Control valves</subject><subject>Convergence</subject><subject>Data Mining and Knowledge Discovery</subject><subject>Feedback control</subject><subject>Image Processing and Computer Vision</subject><subject>Machine learning</subject><subject>Neural networks</subject><subject>Probability and Statistics in Computer Science</subject><subject>S.I: Computational Intelligence-based Control and Estimation in Mechatronic Systems</subject><subject>Sliding mode control</subject><subject>Special Issue on Computational Intelligence-based Control and Estimation in Mechatronic Systems</subject><subject>Trajectory control</subject><subject>Upper bounds</subject><issn>0941-0643</issn><issn>1433-3058</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNp9kM1KAzEUhYMoWKsv4CrgevRmMr_LUvyDghtdD2ly06akk5pkavsGPrYzHcGdi8vlcs93DhxCbhncM4DyIQDkKUtgmIIXeQJnZMIyzhMOeXVOJlBnwyvjl-QqhA0AZEWVT8j3TIldNHukurOWOq_Q02CNMu2Kbp1CKl0bvbNUO0_RouyP1kga197FaJHuhe3pcAwRt_TLxDXV5oCKRrM9wXv0K2wl0i4MnniIHvuPReHbU4iQa9PiNbnQwga8-d1T8vH0-D5_SRZvz6_z2SKRnNUx0VmdCY6CF8uq1nWaqkrxeqmwzDNQrK6wkKWQiKkuS4Ual1khIF1WTFWQ55pPyd3ou_Pus8MQm43rfNtHNmnBa5byvqpelY4q6V0IHnWz82Yr_LFh0AyNN2PjDQwzNN5AD_ERCr24XaH_s_6H-gHAYIeU</recordid><startdate>20220401</startdate><enddate>20220401</enddate><creator>Hu, Youhao</creator><creator>Wang, Hai</creator><creator>Yazdani, Amirmehdi</creator><creator>Man, Zhihong</creator><general>Springer London</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0000-0003-2789-9530</orcidid></search><sort><creationdate>20220401</creationdate><title>Adaptive full order sliding mode control for electronic throttle valve system with fixed time convergence using extreme learning machine</title><author>Hu, Youhao ; Wang, Hai ; Yazdani, Amirmehdi ; Man, Zhihong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c319t-f494a3ea36b89f922d8d39bde7540d198e6c7acee2f77defeb46a02b81d8055f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Adaptive algorithms</topic><topic>Adaptive control</topic><topic>Artificial Intelligence</topic><topic>Artificial neural networks</topic><topic>Closed loop systems</topic><topic>Computational Biology/Bioinformatics</topic><topic>Computational Science and Engineering</topic><topic>Computer Science</topic><topic>Control valves</topic><topic>Convergence</topic><topic>Data Mining and Knowledge Discovery</topic><topic>Feedback control</topic><topic>Image Processing and Computer Vision</topic><topic>Machine learning</topic><topic>Neural networks</topic><topic>Probability and Statistics in Computer Science</topic><topic>S.I: Computational Intelligence-based Control and Estimation in Mechatronic Systems</topic><topic>Sliding mode control</topic><topic>Special Issue on Computational Intelligence-based Control and Estimation in Mechatronic Systems</topic><topic>Trajectory control</topic><topic>Upper bounds</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hu, Youhao</creatorcontrib><creatorcontrib>Wang, Hai</creatorcontrib><creatorcontrib>Yazdani, Amirmehdi</creatorcontrib><creatorcontrib>Man, Zhihong</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Neural computing & applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hu, Youhao</au><au>Wang, Hai</au><au>Yazdani, Amirmehdi</au><au>Man, Zhihong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Adaptive full order sliding mode control for electronic throttle valve system with fixed time convergence using extreme learning machine</atitle><jtitle>Neural computing & applications</jtitle><stitle>Neural Comput & Applic</stitle><date>2022-04-01</date><risdate>2022</risdate><volume>34</volume><issue>7</issue><spage>5241</spage><epage>5253</epage><pages>5241-5253</pages><issn>0941-0643</issn><eissn>1433-3058</eissn><abstract>This paper proposes a novel extreme learning machine (ELM)-based fixed time adaptive trajectory control for electronic throttle valve system with uncertain dynamics and external disturbances. The developed control strategy consists of a recursive full order terminal sliding mode structure based on the bilimit homogeneous property and a lumped uncertainty changing rate upper bound estimator via an adaptive ELM algorithm such that not only the fixed time convergence for both sliding variable and error states can be guaranteed, but also the chattering phenomenon can be suppressed effectively. The stability of the closed-loop system is proved rigorously based on Lyapunov theory. The simulation results are given to verify the superior tracking performance of the proposed control strategy.</abstract><cop>London</cop><pub>Springer London</pub><doi>10.1007/s00521-021-06365-0</doi><tpages>13</tpages><orcidid>https://orcid.org/0000-0003-2789-9530</orcidid></addata></record> |
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| ispartof | Neural computing & applications, 2022-04, Vol.34 (7), p.5241-5253 |
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| subjects | Adaptive algorithms Adaptive control Artificial Intelligence Artificial neural networks Closed loop systems Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Control valves Convergence Data Mining and Knowledge Discovery Feedback control Image Processing and Computer Vision Machine learning Neural networks Probability and Statistics in Computer Science S.I: Computational Intelligence-based Control and Estimation in Mechatronic Systems Sliding mode control Special Issue on Computational Intelligence-based Control and Estimation in Mechatronic Systems Trajectory control Upper bounds |
| title | Adaptive full order sliding mode control for electronic throttle valve system with fixed time convergence using extreme learning machine |
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