Adaptive neural network dynamic surface control of uncertain strict-feedback nonlinear systems with unknown control direction and unknown actuator fault |
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| Institution: | 1. School of Mathematics and Statistics, Fuzhou University, Fuzhou, 350108, China;2. College of Marine Electrical Engineering, Dalian Maritime University, Dalian, 116026, China;1. School of Mathematics Science, Liaocheng University, Liaocheng 252000, PR China;2. School of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, PR China;3. College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao 266590, China;4. School of Electrical and Information Engineering, Anhui University of Technology, Maanshan 243002, China;1. College of Information Engineering, Henan University of Science and Technology, Luoyang, China;2. Henan Key Laboratory of Robot and Intelligent Systems, Henan University of Science and Technology, Luoyang, China |
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| Abstract: | In this paper, the tracking control problem of a class of uncertain strict-feedback nonlinear systems with unknown control direction and unknown actuator fault is studied. By using the neural network control approach and dynamic surface control technique, an adaptive neural network dynamic surface control law is designed. Based on the neural network approximator, the uncertain nonlinear dynamics are approximated. Using the dynamic surface control technique, the complexity explosion problems in the design of virtual control laws and adaptive updating laws can be overcome. Moreover, to solve the unknown control direction and unknown actuator fault problems, a type of Nussbaum gain function is incorporated into the recursive design of dynamic surface control. Based on the designed adaptive control law, it can be confirmed that all of the signals in the closed-loop system are semi-global bounded, and the convergence of the tracking error to the specified small neighborhood of the origin could be ensured by adjusting the designing parameters. Finally, two examples are provided to demonstrate the effectiveness of the proposed adaptive control law. |
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