Adaptive neural asymptotic tracking control for a class of stochastic non-strict-feedback switched systems |
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| Institution: | 1. University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing Normal University, Anqing 246001, China;2. School of Mechanical Engineering, Hunan University of Science & Technology, Xiangtan 411201, China;3. College of Mathematics and Big Data, Anhui University of Science & Technology, Huainan 232001, China;4. National Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China;1. School of Astronautics, Northwestern Polytechnical University, Xi’an 710072, China;2. Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China;1. Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi, Jiangsu 214122, PR China;2. Engineering Research Center of Internet of Things Technology and Applications (Ministry of Education), Jiangnan University, Wuxi, Jiangsu 214122, China;3. Department of Electrical Engineering, Yeungnam University, Kyongsan, Republic of Korea;1. School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China;2. Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, China;3. Key Laboratory of Vehicle Advanced Manufacturing, Measuring and Control Technology (Beijing Jiaotong University), Ministry of Education, China;1. Department of Electrical and Computer Engineering, University of Alberta, Edmonton T6G 2W3, Canada;2. Department of Electrical Engineering, Sahand University of Technology, Tabriz, Iran |
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| Abstract: | The adaptive asymptotic tracking control problem for a class of stochastic non-strict-feedback switched nonlinear systems is addressed in this paper. For the unknown continuous functions, some neural networks are used to approximate them online, and the dynamic surface control (DSC) technique is employed to develop the novel adaptive neural control scheme with the nonlinear filter. The proposed controller ensures that all the closed-loop signals remain semiglobally bounded in probability, at the same time, the output signal asymptotically tracks the desired signal in probability. Finally, a simulation is made to examine the effectiveness of the proposed control scheme. |
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