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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| Published in | IEEE transactions on cybernetics Vol. 52; no. 5; pp. 3408 - 3421 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
IEEE
01.05.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
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| Abstract | 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. |
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| AbstractList | 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. 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.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. |
| Author | Li, Jing Wu, Jian Wu, Zheng-Guang Chen, Xuemiao Zhao, Qianjin |
| Author_xml | – sequence: 1 givenname: Jian orcidid: 0000-0001-7398-6147 surname: Wu fullname: Wu, Jian email: jwu2011@126.com organization: University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing Normal University, Anqing, China – sequence: 2 givenname: Xuemiao surname: Chen fullname: Chen, Xuemiao email: 2537360268@qq.com organization: College of Mathematics and Big Data, Anhui University of Science and Technology, Huainan, China – sequence: 3 givenname: Qianjin surname: Zhao fullname: Zhao, Qianjin email: qjzhao@aust.edu.cn organization: College of Mathematics and Big Data, Anhui University of Science and Technology, Huainan, China – sequence: 4 givenname: Jing orcidid: 0000-0003-3668-1162 surname: Li fullname: Li, Jing email: xidianjing@126.com organization: School of Mathematics and Statistics, Xidian University, Xi'an, China – sequence: 5 givenname: Zheng-Guang orcidid: 0000-0003-4460-9785 surname: Wu fullname: Wu, Zheng-Guang email: nashwzhg@zju.edu.cn organization: National Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32809949$$D View this record in MEDLINE/PubMed |
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| StartPage | 3408 |
| SubjectTerms | Accuracy Adaptive control Adaptive neural control Adaptive systems Artificial neural networks Continuity (mathematics) Control systems design Controllers dynamic surface technique Feedback Fuzzy control MIMO communication Neural networks 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 |
| URI | https://ieeexplore.ieee.org/document/9170878 https://www.ncbi.nlm.nih.gov/pubmed/32809949 https://www.proquest.com/docview/2667016811 https://www.proquest.com/docview/2435529006 |
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