Research on Adaptive Sliding Mode Control Parameter Optimization for Permanent Magnet Synchronous Motor Based on Reinforcement Learning and Simulation Analysis
The permanent magnet synchronous motor (PMSM) has been extensively applied in high-performance drive systems because of its high efficiency, compact construction, and superior dynamic performance. However, the design and implementation of traditional sliding mode control (SMC) systems have to be very dependent on the parameter selection, which could possibly lead to the increase of chattering and decreased adaptability under different operating conditions. In order to solve these problems, an SMC controller parameter optimization method based on reinforcement learning (RL) is put forward in this paper. The proposed RL-based SMC controller parameter optimization algorithm integrates RL into the sliding mode control process, and the adaptive adjustment of the control parameters can be realized through feedback from the performance of the controlled system. Specifically, the system states consist of the speed tracking error, the current error, and the sliding surface characteristic; the optimization goal contains the tracking accuracy, the convergence performance, and the chattering suppression. Simulation experiments on MATLAB/Simulink are performed to validate the effectiveness of the proposed method. The results show that, compared with the traditional SMC, the RL-based adaptive SMC control scheme can achieve good dynamic performance, less steady-state error, chattering reduction, and disturbance rejection.