Adaptive-critic-based model reference control for unknown nonlinear systems with input constraints |
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| Institution: | 1. School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China;2. Hubei Key Laboratory of Digital Textile Equipment, Wuhan Textile University, Wuhan 430200, China;3. Chongqing Key Laboratory of Complex Systems and Bionic Control, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;4. School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, China;1. College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, Liaoning, China;2. Key Laboratory of Technology and System for Intelligent Ships of Liaoning Province, Dalian 116026, Liaoning, China |
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| Abstract: | In this paper, the optimal model reference adaptive control (MRAC) problem is studied for the unknown discrete-time nonlinear systems with input constraint under the premise of considering robustness to uncertainty. Through an input constraint auxiliary system, a new adaptive-critic-based MRAC algorithm is proposed to transform the above problem into the optimal regulation problem of the auxiliary error system with lumped uncertainty. In order to realize the chattering-free sliding model control for the auxiliary error system, an action-critic variable is introduced into the adaptive identification learning. In this case, the closed-loop control system is robust to the disturbance and the neural network approximation error. The uniformly ultimate bounded property is proved by the Lyapunov method, and the effectiveness of the algorithm is verified by a simulation example. |
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