@inproceedings{abfba139f3c846cf8fba3e005acaf06d,
title = "Reinforcement learning control for six-phase permanent magnet synchronous motor position servo drive",
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.",
keywords = "Deep deterministic policy gradient, Reinforcement learning, Servo drive system, Six-phase magnet synchronous motor permanent, Twin delayed deep deterministic policy gradient algorithm",
author = "Peng, {Wei Lun} and Lan, {Yung Wen} and Chen, {Shih Gang} and Lin, {Faa Jeng} and Chang, {Ray I.} and Ho, {Jan Ming}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 3rd IEEE International Conference on Knowledge Innovation and Invention, ICKII 2020 ; Conference date: 21-08-2020 Through 23-08-2020",
year = "2020",
month = aug,
day = "21",
doi = "10.1109/ICKII50300.2020.9318882",
language = "???core.languages.en_GB???",
series = "Proceedings of the 3rd IEEE International Conference on Knowledge Innovation and Invention 2020, ICKII 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "332--335",
editor = "Teen-Hang Meen",
booktitle = "Proceedings of the 3rd IEEE International Conference on Knowledge Innovation and Invention 2020, ICKII 2020",
}