Adaptive Fuzzy Inverse Optimal Control for Uncertain Strict-Feedback Nonlinear Systems
This article first investigates the adaptive fuzzy inverse optimal control design problem for a class of uncertain strict-feedback nonlinear systems. Fuzzy logic systems are utilized to identify the unknown nonlinear dynamics, and then, an equivalent system and an auxiliary system are established. B...
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| Veröffentlicht in: | IEEE transactions on fuzzy systems 2020-10, Vol.28 (10), p.2363-2374 |
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| container_title | IEEE transactions on fuzzy systems |
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| creator | Li, Yong-ming Min, Xiao Tong, Shaocheng |
| description | This article first investigates the adaptive fuzzy inverse optimal control design problem for a class of uncertain strict-feedback nonlinear systems. Fuzzy logic systems are utilized to identify the unknown nonlinear dynamics, and then, an equivalent system and an auxiliary system are established. Based on the auxiliary system and using backstepping recursive design algorithm, an adaptive fuzzy inverse optimal scheme, associating with a meaningful objective functional, is developed. It is proved that the presented adaptive fuzzy inverse optimal control scheme can guarantee that the considered system is input-to-state stabilizable and also achieves the goal of inverse optimality with respect to the cost functional. Finally, the simulation studies and comparisons via two examples are provided to confirm the validity of the developed control strategy. |
| doi_str_mv | 10.1109/TFUZZ.2019.2935693 |
| format | Article |
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Fuzzy logic systems are utilized to identify the unknown nonlinear dynamics, and then, an equivalent system and an auxiliary system are established. Based on the auxiliary system and using backstepping recursive design algorithm, an adaptive fuzzy inverse optimal scheme, associating with a meaningful objective functional, is developed. It is proved that the presented adaptive fuzzy inverse optimal control scheme can guarantee that the considered system is input-to-state stabilizable and also achieves the goal of inverse optimality with respect to the cost functional. Finally, the simulation studies and comparisons via two examples are provided to confirm the validity of the developed control strategy.</description><identifier>ISSN: 1063-6706</identifier><identifier>EISSN: 1941-0034</identifier><identifier>DOI: 10.1109/TFUZZ.2019.2935693</identifier><identifier>CODEN: IEFSEV</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Adaptive algorithms ; Adaptive control ; Adaptive fuzzy control ; Adaptive systems ; Approximation error ; Backstepping ; Dynamical systems ; Feedback ; Fuzzy control ; Fuzzy logic ; Fuzzy systems ; inverse optimal control ; Nonlinear control ; Nonlinear dynamics ; Nonlinear systems ; Optimal control ; Optimization ; strict-feedback ; uncertain nonlinear systems</subject><ispartof>IEEE transactions on fuzzy systems, 2020-10, Vol.28 (10), p.2363-2374</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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Fuzzy logic systems are utilized to identify the unknown nonlinear dynamics, and then, an equivalent system and an auxiliary system are established. Based on the auxiliary system and using backstepping recursive design algorithm, an adaptive fuzzy inverse optimal scheme, associating with a meaningful objective functional, is developed. It is proved that the presented adaptive fuzzy inverse optimal control scheme can guarantee that the considered system is input-to-state stabilizable and also achieves the goal of inverse optimality with respect to the cost functional. Finally, the simulation studies and comparisons via two examples are provided to confirm the validity of the developed control strategy.</description><subject>Adaptive algorithms</subject><subject>Adaptive control</subject><subject>Adaptive fuzzy control</subject><subject>Adaptive systems</subject><subject>Approximation error</subject><subject>Backstepping</subject><subject>Dynamical systems</subject><subject>Feedback</subject><subject>Fuzzy control</subject><subject>Fuzzy logic</subject><subject>Fuzzy systems</subject><subject>inverse optimal control</subject><subject>Nonlinear control</subject><subject>Nonlinear dynamics</subject><subject>Nonlinear systems</subject><subject>Optimal control</subject><subject>Optimization</subject><subject>strict-feedback</subject><subject>uncertain nonlinear systems</subject><issn>1063-6706</issn><issn>1941-0034</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kE1PAjEQhhujiYj-Ab008bzYj-3XkRBXSYgcAA9cmm5pk0XYxbaQwK-3iPE0k8n7zrzzAPCI0QBjpF7m1WK5HBCE1YAoyriiV6CHVYkLhGh5nXvEacEF4rfgLsY1QrhkWPbA53Bldqk5OFjtT6cjHLcHF6KD0zzcmg0cdW0K3Qb6LsBFa11IpmnhLIXGpqJyblUb-wU_unbTtM4EODvG5LbxHtx4s4nu4a_2waJ6nY_ei8n0bTwaTgpLFEuFUgZJ6n3pFa6Vt1xiz5TxQgrCrccGUSUFZwbhWlBf5tSrWlgvuK0JkoL2wfNl7y5033sXk153-9Dmk5qUpaKYlYxmFbmobOhiDM7rXcjfhaPGSJ_56V9--sxP__HLpqeLqXHO_RukzBrC6A9CO2yF</recordid><startdate>20201001</startdate><enddate>20201001</enddate><creator>Li, Yong-ming</creator><creator>Min, Xiao</creator><creator>Tong, Shaocheng</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Fuzzy logic systems are utilized to identify the unknown nonlinear dynamics, and then, an equivalent system and an auxiliary system are established. Based on the auxiliary system and using backstepping recursive design algorithm, an adaptive fuzzy inverse optimal scheme, associating with a meaningful objective functional, is developed. It is proved that the presented adaptive fuzzy inverse optimal control scheme can guarantee that the considered system is input-to-state stabilizable and also achieves the goal of inverse optimality with respect to the cost functional. Finally, the simulation studies and comparisons via two examples are provided to confirm the validity of the developed control strategy.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TFUZZ.2019.2935693</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-4690-7287</orcidid><orcidid>https://orcid.org/0000-0002-5258-2765</orcidid></addata></record> |
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| subjects | Adaptive algorithms Adaptive control Adaptive fuzzy control Adaptive systems Approximation error Backstepping Dynamical systems Feedback Fuzzy control Fuzzy logic Fuzzy systems inverse optimal control Nonlinear control Nonlinear dynamics Nonlinear systems Optimal control Optimization strict-feedback uncertain nonlinear systems |
| title | Adaptive Fuzzy Inverse Optimal Control for Uncertain Strict-Feedback Nonlinear Systems |
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