Adaptive neural network control for nonstrict-feedback uncertain nonlinear systems with input delay and asymmetric time-varying state constraints |
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| Institution: | 1. The Jiangsu Provincial Key Laboratory of Networked Collective Intelligence, and School of Mathematics, Southeast University, Nanjing 211189, China;2. School of Mathematics and Physics, Yancheng Institute of Technology, Yancheng, Jiangsu 224051, China;3. Yonsei Frontier Lab, Yonsei University, Seoul 03722, South Korea;1. School of Science, University of Science and Technology Liaoning, Anshan, Liaoning 114051, PR China;2. School of Mathematical College, Chongqing Normal University, Chongqing 401331, PR China;3. Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu 210023, PR China;4. Control and Computer Engineering, North China Electric Power University, Beijing 102206, PR China;5. Department of Electrical Engineering, Yeungnam University, 280 Daehak-Ro, Kyongsan 38541, Republic of Korea |
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| Abstract: | This paper is devoted to adaptive neural network control issue for a class of nonstrict-feedback uncertain systems with input delay and asymmetric time-varying state constraints. State-related external disturbances are involved into the system, and the upper bounds of disturbances are assumed as functions of state variables instead of constants. Additionally, during the approximations of unknown functions by neural networks, the online computation burdens are declined sharply, since the norms of neural network weight vectors are only estimated. In the process of dealing with input delay, an auxiliary function is applied such that the conditions for time delay are more general than the ones in existing literature. A novel adaptive neural network controller is designed by constructing the asymmetric barrier Lyapunov function, which guarantees that the output of system has a good tracking performance and the state variables never violate the asymmetric time-varying constraints. Finally, numerical simulations are presented to verify the proposed adaptive control scheme. |
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