Reinforcement learning control for six-phase permanent magnet synchronous motor position servo drive
Journal
Proceedings of the 3rd IEEE International Conference on Knowledge Innovation and Invention 2020, ICKII 2020
Pages
332-335
Date Issued
2020
Author(s)
Abstract
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. ? 2020 IEEE.
Subjects
Controllers; Deep learning; Patents and inventions; Permanent magnets; Reinforcement learning; Synchronous motors; Cerebellar model articulation; Comparison study; Ideal controllers; Nonlinear dynamic behaviors; Permanent Magnet Synchronous Motor; Reference trajectories; Reinforcement learning control; Tracking errors; Electric machine control
SDGs
Type
conference paper