Adaptive neural network control of unknown nonlinear affine systems with input deadzone and output constraint |
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| Affiliation: | 1. School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China;2. Department of Electrical and Electronic Engineering, Imperial College London, UK;3. School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 10083, China;1. School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China;2. Donlinks School of Economics and Management, University of Science and Technology Beijing, Beijing, 100083, China;1. School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China;2. School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China |
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| Abstract: | In this paper, we aim to solve the control problem of nonlinear affine systems, under the condition of the input deadzone and output constraint with the external unknown disturbance. To eliminate the effects of the input deadzone, a Radial Basis Function Neural Network (RBFNN) is introduced to compensate for the negative impact of input deadzone. Meanwhile, we design a barrier Lyapunov function to ensure that the output parameters are restricted. In support of the barrier Lyapunov method, we build an adaptive neural network controller based on state feedback and output feedback methods. The stability of the closed-loop system is proven via the Lyapunov method and the performance of the expected effects is verified in simulation. |
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| Keywords: | Unknown nonlinear affine system Input deadzone Output constraint Radial Basis Function Neural Network (RBFNN) Barrier Lyapunov Function |
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