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A Novel Adaptive Neuro-Control Approach for Permanent Magnet Synchronous Motor Speed Control

Author

Listed:
  • Qi Wang

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China
    Department of Automatic Control, Henan Institute of Technology, Xinxiang 453003, China)

  • Haitao Yu

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Min Wang

    (School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China)

  • Xinbo Qi

    (Department of Automatic Control, Henan Institute of Technology, Xinxiang 453003, China)

Abstract

A speed controller for permanent magnet synchronous motors (PMSMs) under the field oriented control (FOC) method is discussed in this paper. First, a novel adaptive neuro-control approach, single artificial neuron goal representation heuristic dynamic programming (SAN-GrHDP) for speed regulation of PMSMs, is presented. For both current loops, PI controllers are adopted, respectively. Compared with the conventional single artificial neuron (SAN) control strategy, the proposed approach assumes an unknown mathematic model of the PMSM and guides the selection value of parameter K online. Besides, the proposed design can develop an internal reinforcement learning signal to guide the dynamic optimal control of the PMSM in the process. Finally, nonlinear optimal control simulations and experiments on the speed regulation of a PMSM are implemented in Matlab2016a and TMS320F28335, a 32-bit floating-point digital signal processor (DSP), respectively. To achieve a comparative study, the conventional SAN and SAN-GrHDP approaches are set up under identical conditions and parameters. Simulation and experiment results verify that the proposed controller can improve the speed control performance of PMSMs.

Suggested Citation

  • Qi Wang & Haitao Yu & Min Wang & Xinbo Qi, 2018. "A Novel Adaptive Neuro-Control Approach for Permanent Magnet Synchronous Motor Speed Control," Energies, MDPI, vol. 11(9), pages 1-21, September.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:9:p:2355-:d:168154
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    References listed on IDEAS

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    1. Kyoung Jin Joo & Joon Sung Park & Ju Lee, 2018. "Study on Reduced Cost of Non-Salient Machine System Using MTPA Angle Pre-Compensation Method Based on EEMF Sensorless Control," Energies, MDPI, vol. 11(6), pages 1-14, June.
    2. Dandan Su & Chengning Zhang & Yugang Dong, 2017. "An Improved Continuous-Time Model Predictive Control of Permanent Magnetic Synchronous Motors for a Wide-Speed Range," Energies, MDPI, vol. 10(12), pages 1-18, December.
    3. Joon B. Park & Xin Wang, 2018. "Sensorless Direct Torque Control of Surface-Mounted Permanent Magnet Synchronous Motors with Nonlinear Kalman Filtering," Energies, MDPI, vol. 11(4), pages 1-19, April.
    4. Ming Yang & Zirui Liu & Jiang Long & Wanying Qu & Dianguo Xu, 2018. "An Algorithm for Online Inertia Identification and Load Torque Observation via Adaptive Kalman Observer-Recursive Least Squares," Energies, MDPI, vol. 11(4), pages 1-17, March.
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    Cited by:

    1. Fardila Mohd Zaihidee & Saad Mekhilef & Marizan Mubin, 2019. "Robust Speed Control of PMSM Using Sliding Mode Control (SMC)—A Review," Energies, MDPI, vol. 12(9), pages 1-27, May.
    2. Roland Kasper & Dmytro Golovakha, 2020. "Combined Optimal Torque Feedforward and Modal Current Feedback Control for Low Inductance PM Motors," Energies, MDPI, vol. 13(23), pages 1-16, November.

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