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Deep reinforcement learning for permanent magnet synchronous motor speed control systems

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Abstract

The permanent magnet synchronous motor (PMSM) servo system is widely applied in many industrial fields due to its unique advantages. In this paper, we study the deep reinforcement learning (DRL) speed control strategy for PMSM servo system, in which exist many disturbances, i.e., load torque and rotational inertia variations. The speed control problem is formulated as a Markov decision process problem, which is computed optimal regulation scheme corresponding to each speed and error state using the deep Q-networks. Simulation results are provided to demonstrate that compared with conventional proportion integral control, the proposed DRL control can improve the robustness against load disturbances and high performance of the PMSM speed control system.

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Acknowledgements

This work was financially supported by the National Key Research and Development Program of China (2016YFB1102503), Research Project of State Key Lab of Digital Manufacturing Equipment and Technology (DMETKF2019017), and the National Natural Science Foundation of China (No. 51605375). This work was also supported in part by the Science and Technology Co-ordination and Innovation Program of Shaanxi Province, China, under Grant 2015KTZDGY-02-01.

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Authors and Affiliations

  1. State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an, 710049, Shaanxi, China

    Zhe Song, Jun Yang, Xuesong Mei, Tao Tao & Muxun Xu

  2. Department of Electrical Engineering, Columbia University, New York, NY, 10027, USA

    Zhe Song

  3. State Key Lab of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China

    Jun Yang

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  1. Zhe Song
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  2. Jun Yang
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  3. Xuesong Mei
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  4. Tao Tao
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  5. Muxun Xu
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Correspondence to Zhe Song.

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Song, Z., Yang, J., Mei, X. et al. Deep reinforcement learning for permanent magnet synchronous motor speed control systems. Neural Comput & Applic 33, 5409–5418 (2021). https://doi.org/10.1007/s00521-020-05352-1

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