Optimal Guaranteed Cost Sliding Mode Control for Constrained-Input Nonlinear Systems With Matched and Unmatched Disturbances
Based on integral sliding mode and approximate dynamic programming (ADP) theory, a novel optimal guaranteed cost sliding mode control is designed for constrained-input nonlinear systems with matched and unmatched disturbances. When the system moves on the sliding surface, the optimal guaranteed cost...
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| Veröffentlicht in: | IEEE transaction on neural networks and learning systems 2018-06, Vol.29 (6), p.2112-2126 |
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| container_title | IEEE transaction on neural networks and learning systems |
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| creator | Zhang, Huaguang Qu, Qiuxia Xiao, Geyang Cui, Yang |
| description | Based on integral sliding mode and approximate dynamic programming (ADP) theory, a novel optimal guaranteed cost sliding mode control is designed for constrained-input nonlinear systems with matched and unmatched disturbances. When the system moves on the sliding surface, the optimal guaranteed cost control problem of sliding mode dynamics is transformed into the optimal control problem of a reformulated auxiliary system with a modified cost function. The ADP algorithm based on single critic neural network (NN) is applied to obtain the approximate optimal control law for the auxiliary system. Lyapunov techniques are used to demonstrate the convergence of the NN weight errors. In addition, the derived approximate optimal control is verified to guarantee the sliding mode dynamics system to be stable in the sense of uniform ultimate boundedness. Some simulation results are presented to verify the feasibility of the proposed control scheme. |
| doi_str_mv | 10.1109/TNNLS.2018.2791419 |
| format | Article |
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When the system moves on the sliding surface, the optimal guaranteed cost control problem of sliding mode dynamics is transformed into the optimal control problem of a reformulated auxiliary system with a modified cost function. The ADP algorithm based on single critic neural network (NN) is applied to obtain the approximate optimal control law for the auxiliary system. Lyapunov techniques are used to demonstrate the convergence of the NN weight errors. In addition, the derived approximate optimal control is verified to guarantee the sliding mode dynamics system to be stable in the sense of uniform ultimate boundedness. 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(IEEE) 2018</rights><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c351t-26e507c566bad9250d884838c3e692ed4ace361d0f2b3c737b7d98148ff1e9d93</citedby><cites>FETCH-LOGICAL-c351t-26e507c566bad9250d884838c3e692ed4ace361d0f2b3c737b7d98148ff1e9d93</cites><orcidid>0000-0002-0647-4050</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8275509$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>315,781,785,797,27926,27927,54760</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8275509$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29771665$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Zhang, Huaguang</creatorcontrib><creatorcontrib>Qu, Qiuxia</creatorcontrib><creatorcontrib>Xiao, Geyang</creatorcontrib><creatorcontrib>Cui, Yang</creatorcontrib><title>Optimal Guaranteed Cost Sliding Mode Control for Constrained-Input Nonlinear Systems With Matched and Unmatched Disturbances</title><title>IEEE transaction on neural networks and learning systems</title><addtitle>TNNLS</addtitle><addtitle>IEEE Trans Neural Netw Learn Syst</addtitle><description>Based on integral sliding mode and approximate dynamic programming (ADP) theory, a novel optimal guaranteed cost sliding mode control is designed for constrained-input nonlinear systems with matched and unmatched disturbances. 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Some simulation results are presented to verify the feasibility of the proposed control scheme.</description><subject>Adaptive dynamic programming</subject><subject>Algorithm design and analysis</subject><subject>Approximation algorithms</subject><subject>Artificial neural networks</subject><subject>Computer simulation</subject><subject>Control systems</subject><subject>Control theory</subject><subject>Disturbances</subject><subject>Dynamic programming</subject><subject>Dynamical systems</subject><subject>Feasibility studies</subject><subject>Neural networks</subject><subject>Nonlinear control</subject><subject>Nonlinear systems</subject><subject>Optimal control</subject><subject>optimal guaranteed cost</subject><subject>Robustness</subject><subject>single neural network (NN)</subject><subject>Sliding mode control</subject><subject>sliding mode control (SMC)</subject><subject>unmatched disturbance</subject><subject>Weight</subject><issn>2162-237X</issn><issn>2162-2388</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkUtv1DAURiMEolXpHwAJWWLDJlM_Ej-WaAql0nS6mFbtznLsG5oqsQfbWVTix-NhhlnUG99rn_vJ8qmqjwQvCMHq4m69Xm0WFBO5oEKRhqg31SklnNaUSfn2WIvHk-o8pWdcFsctb9T76oQqIQjn7Wn153abh8mM6Go20fgM4NAypIw24-AG_wvdBAflxOcYRtSHuKtTjmbw4Oprv50zWgc_ltZEtHlJGaaEHob8hG5Mtk8lzniH7v106C6HlOfYGW8hfaje9WZMcH7Yz6r7H9_vlj_r1e3V9fLbqrasJbmmHFosbMt5Z5yiLXZSNpJJy4ArCq4xFhgnDve0Y1Yw0QmnJGlk3xNQTrGz6us-dxvD7xlS1tOQLIyj8RDmpCluCGcEY1nQL6_Q5zBHX16nKRFN23DBaKHonrIxpBSh19tYfjG-aIL1To_-p0fv9OiDnjL0-RA9dxO448h_GQX4tAcGADheSyraFiv2F1ZmlHI</recordid><startdate>20180601</startdate><enddate>20180601</enddate><creator>Zhang, Huaguang</creator><creator>Qu, Qiuxia</creator><creator>Xiao, Geyang</creator><creator>Cui, Yang</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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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>Zhang, Huaguang</au><au>Qu, Qiuxia</au><au>Xiao, Geyang</au><au>Cui, Yang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Optimal Guaranteed Cost Sliding Mode Control for Constrained-Input Nonlinear Systems With Matched and Unmatched Disturbances</atitle><jtitle>IEEE transaction on neural networks and learning systems</jtitle><stitle>TNNLS</stitle><addtitle>IEEE Trans Neural Netw Learn Syst</addtitle><date>2018-06-01</date><risdate>2018</risdate><volume>29</volume><issue>6</issue><spage>2112</spage><epage>2126</epage><pages>2112-2126</pages><issn>2162-237X</issn><eissn>2162-2388</eissn><coden>ITNNAL</coden><abstract>Based on integral sliding mode and approximate dynamic programming (ADP) theory, a novel optimal guaranteed cost sliding mode control is designed for constrained-input nonlinear systems with matched and unmatched disturbances. When the system moves on the sliding surface, the optimal guaranteed cost control problem of sliding mode dynamics is transformed into the optimal control problem of a reformulated auxiliary system with a modified cost function. The ADP algorithm based on single critic neural network (NN) is applied to obtain the approximate optimal control law for the auxiliary system. Lyapunov techniques are used to demonstrate the convergence of the NN weight errors. In addition, the derived approximate optimal control is verified to guarantee the sliding mode dynamics system to be stable in the sense of uniform ultimate boundedness. Some simulation results are presented to verify the feasibility of the proposed control scheme.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>29771665</pmid><doi>10.1109/TNNLS.2018.2791419</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0002-0647-4050</orcidid></addata></record> |
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| subjects | Adaptive dynamic programming Algorithm design and analysis Approximation algorithms Artificial neural networks Computer simulation Control systems Control theory Disturbances Dynamic programming Dynamical systems Feasibility studies Neural networks Nonlinear control Nonlinear systems Optimal control optimal guaranteed cost Robustness single neural network (NN) Sliding mode control sliding mode control (SMC) unmatched disturbance Weight |
| title | Optimal Guaranteed Cost Sliding Mode Control for Constrained-Input Nonlinear Systems With Matched and Unmatched Disturbances |
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