Reinforcement Learning Control for Six-Phase Permanent Magnet Synchronous Motor Position Servo Drive

Since the permanent magnet synchronous motor (PMSM) has nonlinear dynamic behavior characteristics, it is difficult to develop an ideal controller. In this paper, we develop a novel method for the six-phase PMSM (6PPMSM) position servo drive based on deep reinforcement learning (RL). Comparison studies between the proposed controller and the recurrent fuzzy neural cerebellar model articulation network (RFNCMAN) controller are presented. The results show that our controller can follow the reference trajectories more precisely in general cases, where the average tracking error obtained is 90% smaller than that of RFNCMAN.