Adaptive Neural Dynamic Surface Control With Prespecified Tracking Accuracy of Uncertain Stochastic Nonstrict-Feedback Systems
This article addresses the adaptive neural tracking control problem for a class of uncertain stochastic nonlinear systems with nonstrict-feedback form and prespecified tracking accuracy. Some radial basis function neural networks (RBF NNs) are used to approximate the unknown continuous functions onl...
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| Veröffentlicht in: | IEEE transactions on cybernetics 2022-05, Vol.52 (5), p.3408-3421 |
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| container_title | IEEE transactions on cybernetics |
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| creator | Wu, Jian Chen, Xuemiao Zhao, Qianjin Li, Jing Wu, Zheng-Guang |
| description | This article addresses the adaptive neural tracking control problem for a class of uncertain stochastic nonlinear systems with nonstrict-feedback form and prespecified tracking accuracy. Some radial basis function neural networks (RBF NNs) are used to approximate the unknown continuous functions online, and the desired controller is designed via the adaptive dynamic surface control (DSC) method and the gain suppressing inequality technique. Different from the reported works on uncertain stochastic systems, by combining some non-negative switching functions and dynamic surface method with the nonlinear filter, the design difficulty is overcome, and the control performance is analyzed by employing stochastic Barbalat's lemma. Under the constructed controller, the tracking error converges to the accuracy defined a priori in probability. The simulation results are shown to verify the availability of the presented control scheme. |
| doi_str_mv | 10.1109/TCYB.2020.3012607 |
| format | Article |
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Some radial basis function neural networks (RBF NNs) are used to approximate the unknown continuous functions online, and the desired controller is designed via the adaptive dynamic surface control (DSC) method and the gain suppressing inequality technique. Different from the reported works on uncertain stochastic systems, by combining some non-negative switching functions and dynamic surface method with the nonlinear filter, the design difficulty is overcome, and the control performance is analyzed by employing stochastic Barbalat's lemma. Under the constructed controller, the tracking error converges to the accuracy defined a priori in probability. The simulation results are shown to verify the availability of the presented control scheme.</description><identifier>ISSN: 2168-2267</identifier><identifier>EISSN: 2168-2275</identifier><identifier>DOI: 10.1109/TCYB.2020.3012607</identifier><identifier>PMID: 32809949</identifier><identifier>CODEN: ITCEB8</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Accuracy ; Adaptive control ; Adaptive neural control ; Adaptive systems ; Artificial neural networks ; Computer Simulation ; Continuity (mathematics) ; Control systems design ; Controllers ; dynamic surface technique ; Feedback ; Fuzzy control ; MIMO communication ; Neural networks ; Neural Networks, Computer ; Nonlinear Dynamics ; Nonlinear filters ; Nonlinear systems ; prespecified tracking accuracy ; Radial basis function ; stochastic nonstrict-feedback systems ; Stochastic systems ; Switches ; Tracking control ; Tracking errors</subject><ispartof>IEEE transactions on cybernetics, 2022-05, Vol.52 (5), p.3408-3421</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2022</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c349t-fa0eba91b9bc4149b13977e223c73855727fa719ebab408966b581138be517b93</citedby><cites>FETCH-LOGICAL-c349t-fa0eba91b9bc4149b13977e223c73855727fa719ebab408966b581138be517b93</cites><orcidid>0000-0003-4460-9785 ; 0000-0001-7398-6147 ; 0000-0003-3668-1162</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9170878$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9170878$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32809949$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wu, Jian</creatorcontrib><creatorcontrib>Chen, Xuemiao</creatorcontrib><creatorcontrib>Zhao, Qianjin</creatorcontrib><creatorcontrib>Li, Jing</creatorcontrib><creatorcontrib>Wu, Zheng-Guang</creatorcontrib><title>Adaptive Neural Dynamic Surface Control With Prespecified Tracking Accuracy of Uncertain Stochastic Nonstrict-Feedback Systems</title><title>IEEE transactions on cybernetics</title><addtitle>TCYB</addtitle><addtitle>IEEE Trans Cybern</addtitle><description>This article addresses the adaptive neural tracking control problem for a class of uncertain stochastic nonlinear systems with nonstrict-feedback form and prespecified tracking accuracy. Some radial basis function neural networks (RBF NNs) are used to approximate the unknown continuous functions online, and the desired controller is designed via the adaptive dynamic surface control (DSC) method and the gain suppressing inequality technique. Different from the reported works on uncertain stochastic systems, by combining some non-negative switching functions and dynamic surface method with the nonlinear filter, the design difficulty is overcome, and the control performance is analyzed by employing stochastic Barbalat's lemma. Under the constructed controller, the tracking error converges to the accuracy defined a priori in probability. The simulation results are shown to verify the availability of the presented control scheme.</description><subject>Accuracy</subject><subject>Adaptive control</subject><subject>Adaptive neural control</subject><subject>Adaptive systems</subject><subject>Artificial neural networks</subject><subject>Computer Simulation</subject><subject>Continuity (mathematics)</subject><subject>Control systems design</subject><subject>Controllers</subject><subject>dynamic surface technique</subject><subject>Feedback</subject><subject>Fuzzy control</subject><subject>MIMO communication</subject><subject>Neural networks</subject><subject>Neural Networks, Computer</subject><subject>Nonlinear Dynamics</subject><subject>Nonlinear filters</subject><subject>Nonlinear systems</subject><subject>prespecified tracking accuracy</subject><subject>Radial basis function</subject><subject>stochastic nonstrict-feedback systems</subject><subject>Stochastic systems</subject><subject>Switches</subject><subject>Tracking control</subject><subject>Tracking errors</subject><issn>2168-2267</issn><issn>2168-2275</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNpdkU9rFDEYh4Motqz9ACJIwIuXWfNnZpIc19WqUNrCbhFPIcm-Y1NnJtskI-zFz94su-6huSQkz-_HSx6E3lIyp5SoT-vlr89zRhiZc0JZS8QLdM5oKyvGRPPydG7FGbpI6YGUJcuVkq_RGWeSKFWrc_RvsTHb7P8CvoYpmh5_2Y1m8A6vptgZB3gZxhxDj3_6fI9vI6QtON952OB1NO6PH3_jhXMl6nY4dPhudBCz8SNe5eDuTcql6zqMKUfvcnUJsLElhle7lGFIb9CrzvQJLo77DN1dfl0vv1dXN99-LBdXleO1ylVnCFijqFXW1bRWlnIlBDDGneCyaQQTnRFUFcjWRKq2tY2klEsLDRVW8Rn6eOjdxvA4Qcp68MlB35sRwpQ0q3nTMEVIW9APz9CHMMWxTKdZ2wpS_rA0zxA9UC6GlCJ0ehv9YOJOU6L3fvTej9770Uc_JfP-2DzZATanxH8bBXh3ADwAnJ4VFUQKyZ8AFeGUEQ</recordid><startdate>20220501</startdate><enddate>20220501</enddate><creator>Wu, Jian</creator><creator>Chen, Xuemiao</creator><creator>Zhao, Qianjin</creator><creator>Li, Jing</creator><creator>Wu, Zheng-Guang</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Some radial basis function neural networks (RBF NNs) are used to approximate the unknown continuous functions online, and the desired controller is designed via the adaptive dynamic surface control (DSC) method and the gain suppressing inequality technique. Different from the reported works on uncertain stochastic systems, by combining some non-negative switching functions and dynamic surface method with the nonlinear filter, the design difficulty is overcome, and the control performance is analyzed by employing stochastic Barbalat's lemma. Under the constructed controller, the tracking error converges to the accuracy defined a priori in probability. 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| subjects | Accuracy Adaptive control Adaptive neural control Adaptive systems Artificial neural networks Computer Simulation Continuity (mathematics) Control systems design Controllers dynamic surface technique Feedback Fuzzy control MIMO communication Neural networks Neural Networks, Computer Nonlinear Dynamics Nonlinear filters Nonlinear systems prespecified tracking accuracy Radial basis function stochastic nonstrict-feedback systems Stochastic systems Switches Tracking control Tracking errors |
| title | Adaptive Neural Dynamic Surface Control With Prespecified Tracking Accuracy of Uncertain Stochastic Nonstrict-Feedback Systems |
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