Neural-network-based approach to finite-time optimal control for a class of unknown nonlinear systems
This paper proposes a novel finite-time optimal control method based on input–output data for unknown nonlinear systems using adaptive dynamic programming (ADP) algorithm. In this method, the single-hidden layer feed-forward network (SLFN) with extreme learning machine (ELM) is used to construct the...
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| Veröffentlicht in: | Soft computing (Berlin, Germany) Germany), 2014-08, Vol.18 (8), p.1645-1653 |
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| container_title | Soft computing (Berlin, Germany) |
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| creator | Song, Ruizhuo Xiao, Wendong Wei, Qinglai Sun, Changyin |
| description | This paper proposes a novel finite-time optimal control method based on input–output data for unknown nonlinear systems using adaptive dynamic programming (ADP) algorithm. In this method, the single-hidden layer feed-forward network (SLFN) with extreme learning machine (ELM) is used to construct the data-based identifier of the unknown system dynamics. Based on the data-based identifier, the finite-time optimal control method is established by ADP algorithm. Two other SLFNs with ELM are used in ADP method to facilitate the implementation of the iterative algorithm, which aim to approximate the performance index function and the optimal control law at each iteration, respectively. A simulation example is provided to demonstrate the effectiveness of the proposed control scheme. |
| doi_str_mv | 10.1007/s00500-013-1170-z |
| format | Article |
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In this method, the single-hidden layer feed-forward network (SLFN) with extreme learning machine (ELM) is used to construct the data-based identifier of the unknown system dynamics. Based on the data-based identifier, the finite-time optimal control method is established by ADP algorithm. Two other SLFNs with ELM are used in ADP method to facilitate the implementation of the iterative algorithm, which aim to approximate the performance index function and the optimal control law at each iteration, respectively. A simulation example is provided to demonstrate the effectiveness of the proposed control scheme.</description><identifier>ISSN: 1432-7643</identifier><identifier>EISSN: 1433-7479</identifier><identifier>DOI: 10.1007/s00500-013-1170-z</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Adaptive systems ; Algorithms ; Artificial Intelligence ; Artificial neural networks ; Computational Intelligence ; Control ; Control methods ; Control theory ; Controllers ; Design ; Dynamic programming ; Engineering ; Euclidean space ; Iterative algorithms ; Machine learning ; Mathematical Logic and Foundations ; Mechatronics ; Methodologies and Application ; Neural networks ; Neurons ; Nonlinear systems ; Performance indices ; Robotics ; System dynamics ; Time optimal control</subject><ispartof>Soft computing (Berlin, Germany), 2014-08, Vol.18 (8), p.1645-1653</ispartof><rights>Springer-Verlag Berlin Heidelberg 2013</rights><rights>Springer-Verlag Berlin Heidelberg 2013.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c349t-9a440b206c18f58d7b8a38f85caf5bbad09bb0dac42e2af90872e3853fdbfb6e3</citedby><cites>FETCH-LOGICAL-c349t-9a440b206c18f58d7b8a38f85caf5bbad09bb0dac42e2af90872e3853fdbfb6e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00500-013-1170-z$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2918032687?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,776,780,21368,27903,27904,33723,41467,42536,43784,51297,64361,64365,72215</link.rule.ids></links><search><creatorcontrib>Song, Ruizhuo</creatorcontrib><creatorcontrib>Xiao, Wendong</creatorcontrib><creatorcontrib>Wei, Qinglai</creatorcontrib><creatorcontrib>Sun, Changyin</creatorcontrib><title>Neural-network-based approach to finite-time optimal control for a class of unknown nonlinear systems</title><title>Soft computing (Berlin, Germany)</title><addtitle>Soft Comput</addtitle><description>This paper proposes a novel finite-time optimal control method based on input–output data for unknown nonlinear systems using adaptive dynamic programming (ADP) algorithm. In this method, the single-hidden layer feed-forward network (SLFN) with extreme learning machine (ELM) is used to construct the data-based identifier of the unknown system dynamics. Based on the data-based identifier, the finite-time optimal control method is established by ADP algorithm. Two other SLFNs with ELM are used in ADP method to facilitate the implementation of the iterative algorithm, which aim to approximate the performance index function and the optimal control law at each iteration, respectively. A simulation example is provided to demonstrate the effectiveness of the proposed control scheme.</description><subject>Adaptive systems</subject><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Artificial neural networks</subject><subject>Computational Intelligence</subject><subject>Control</subject><subject>Control methods</subject><subject>Control theory</subject><subject>Controllers</subject><subject>Design</subject><subject>Dynamic programming</subject><subject>Engineering</subject><subject>Euclidean space</subject><subject>Iterative algorithms</subject><subject>Machine learning</subject><subject>Mathematical Logic and Foundations</subject><subject>Mechatronics</subject><subject>Methodologies and Application</subject><subject>Neural networks</subject><subject>Neurons</subject><subject>Nonlinear systems</subject><subject>Performance indices</subject><subject>Robotics</subject><subject>System dynamics</subject><subject>Time optimal control</subject><issn>1432-7643</issn><issn>1433-7479</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kM1OwzAQhCMEEqXwANwscTas7SR2jqjiT0JwgbNlJzakTe1iO6rap8clSJw4zR5mZne_orgkcE0A-E0EqAAwEIYJ4YD3R8WMlIxhXvLm-GemmNclOy3OYlwCUMIrNivMixmDGrAzaevDCmsVTYfUZhO8aj9R8sj2rk8Gp35tkN9kUQNqvUvBD8j6gBRqBxUj8haNbuX81iHn3dA7owKKu5jMOp4XJ1YN0Vz86rx4v797Wzzi59eHp8XtM25Z2STcqLIETaFuibCV6LgWigkrqlbZSmvVQaM1dKotqaHKNiA4NUxUzHba6tqweXE19ebzv0YTk1z6Mbi8UtKGCGC0Fjy7yORqg48xGCs3Ib8VdpKAPNCUE02ZacoDTbnPGTplYva6DxP-mv8PfQPua3pz</recordid><startdate>20140801</startdate><enddate>20140801</enddate><creator>Song, Ruizhuo</creator><creator>Xiao, Wendong</creator><creator>Wei, Qinglai</creator><creator>Sun, Changyin</creator><general>Springer Berlin Heidelberg</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>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope></search><sort><creationdate>20140801</creationdate><title>Neural-network-based approach to finite-time optimal control for a class of unknown nonlinear systems</title><author>Song, Ruizhuo ; Xiao, Wendong ; Wei, Qinglai ; Sun, Changyin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c349t-9a440b206c18f58d7b8a38f85caf5bbad09bb0dac42e2af90872e3853fdbfb6e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Adaptive systems</topic><topic>Algorithms</topic><topic>Artificial Intelligence</topic><topic>Artificial neural networks</topic><topic>Computational Intelligence</topic><topic>Control</topic><topic>Control methods</topic><topic>Control theory</topic><topic>Controllers</topic><topic>Design</topic><topic>Dynamic programming</topic><topic>Engineering</topic><topic>Euclidean space</topic><topic>Iterative algorithms</topic><topic>Machine learning</topic><topic>Mathematical Logic and Foundations</topic><topic>Mechatronics</topic><topic>Methodologies and Application</topic><topic>Neural networks</topic><topic>Neurons</topic><topic>Nonlinear systems</topic><topic>Performance indices</topic><topic>Robotics</topic><topic>System dynamics</topic><topic>Time optimal control</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Song, Ruizhuo</creatorcontrib><creatorcontrib>Xiao, Wendong</creatorcontrib><creatorcontrib>Wei, Qinglai</creatorcontrib><creatorcontrib>Sun, Changyin</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 Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</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><jtitle>Soft computing (Berlin, Germany)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Song, Ruizhuo</au><au>Xiao, Wendong</au><au>Wei, Qinglai</au><au>Sun, Changyin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Neural-network-based approach to finite-time optimal control for a class of unknown nonlinear systems</atitle><jtitle>Soft computing (Berlin, Germany)</jtitle><stitle>Soft Comput</stitle><date>2014-08-01</date><risdate>2014</risdate><volume>18</volume><issue>8</issue><spage>1645</spage><epage>1653</epage><pages>1645-1653</pages><issn>1432-7643</issn><eissn>1433-7479</eissn><abstract>This paper proposes a novel finite-time optimal control method based on input–output data for unknown nonlinear systems using adaptive dynamic programming (ADP) algorithm. In this method, the single-hidden layer feed-forward network (SLFN) with extreme learning machine (ELM) is used to construct the data-based identifier of the unknown system dynamics. Based on the data-based identifier, the finite-time optimal control method is established by ADP algorithm. Two other SLFNs with ELM are used in ADP method to facilitate the implementation of the iterative algorithm, which aim to approximate the performance index function and the optimal control law at each iteration, respectively. A simulation example is provided to demonstrate the effectiveness of the proposed control scheme.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s00500-013-1170-z</doi><tpages>9</tpages></addata></record> |
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| ispartof | Soft computing (Berlin, Germany), 2014-08, Vol.18 (8), p.1645-1653 |
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| subjects | Adaptive systems Algorithms Artificial Intelligence Artificial neural networks Computational Intelligence Control Control methods Control theory Controllers Design Dynamic programming Engineering Euclidean space Iterative algorithms Machine learning Mathematical Logic and Foundations Mechatronics Methodologies and Application Neural networks Neurons Nonlinear systems Performance indices Robotics System dynamics Time optimal control |
| title | Neural-network-based approach to finite-time optimal control for a class of unknown nonlinear systems |
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