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Interior Permanent Magnet Synchronous Motor Drive System with Machine Learning-Based Maximum Torque per Ampere and Flux-Weakening Control

Author

Listed:
  • Faa-Jeng Lin

    (Department of Electrical Engineering, National Central University, Taoyuan 320, Taiwan)

  • Yi-Hung Liao

    (Department of Electrical Engineering, National Central University, Taoyuan 320, Taiwan)

  • Jyun-Ru Lin

    (Department of Electrical Engineering, National Central University, Taoyuan 320, Taiwan)

  • Wei-Ting Lin

    (Department of Electrical Engineering, National Central University, Taoyuan 320, Taiwan)

Abstract

An interior permanent magnet synchronous motor (IPMSM) drive system with machine learning-based maximum torque per ampere (MTPA) as well as flux-weakening (FW) control was developed and is presented in this study. Since the control performance of IPMSM varies significantly due to the temperature variation and magnetic saturation, a machine learning-based MTPA control using a Petri probabilistic fuzzy neural network with an asymmetric membership function (PPFNN-AMF) was developed. First, the d -axis current command, which can achieve the MTPA control of the IPMSM, is derived. Then, the difference value of the dq -axis inductance of the IPMSM is obtained by the PPFNN-AMF and substituted into the d -axis current command of the MTPA to alleviate the saturation effect in the constant torque region. Moreover, a voltage control loop, which can limit the inverter output voltage to the maximum output voltage of the inverter at high-speed, is designed for the FW control in the constant power region. In addition, an adaptive complementary sliding mode (ACSM) speed controller is developed to improve the transient response of the speed control. Finally, some experimental results are given to demonstrate the validity of the proposed high-performance control strategies.

Suggested Citation

  • Faa-Jeng Lin & Yi-Hung Liao & Jyun-Ru Lin & Wei-Ting Lin, 2021. "Interior Permanent Magnet Synchronous Motor Drive System with Machine Learning-Based Maximum Torque per Ampere and Flux-Weakening Control," Energies, MDPI, vol. 14(2), pages 1-24, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:2:p:346-:d:477682
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    References listed on IDEAS

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    1. Dooyoung Yang & Hyungsoo Mok & Jusuk Lee & Soohee Han, 2017. "Adaptive Torque Estimation for an IPMSM with Cross-Coupling and Parameter Variations," Energies, MDPI, vol. 10(2), pages 1-13, January.
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    Cited by:

    1. Nuofan Zou & Yan Yan & Tingna Shi & Peng Song, 2021. "Wide Speed Range Operation Strategy of Indirect Matrix Converter–Surface Mounted Permanent Magnet Synchronous Motor Drive," Energies, MDPI, vol. 14(8), pages 1-24, April.
    2. Federico Barrero & Jorge Rodas, 2021. "Control of Power Electronics Converters and Electric Motor Drives," Energies, MDPI, vol. 14(15), pages 1-2, July.
    3. Kai-Hung Lu & Chih-Ming Hong & Fu-Sheng Cheng, 2022. "Enhanced Dynamic Performance in Hybrid Power System Using a Designed ALTS-PFPNN Controller," Energies, MDPI, vol. 15(21), pages 1-22, November.
    4. Yang Liu & Jin Zhao & Quan Yin, 2021. "Model-Based Predictive Rotor Field-Oriented Angle Compensation for Induction Machine Drives," Energies, MDPI, vol. 14(8), pages 1-13, April.
    5. Zhaozhi Wang & Shemeng Wu & Kai-Hung Lu, 2022. "Improvement of Stability in an Oscillating Water Column Wave Energy Using an Adaptive Intelligent Controller," Energies, MDPI, vol. 16(1), pages 1-15, December.
    6. Faa-Jeng Lin & Syuan-Yi Chen & Wei-Ting Lin & Chih-Wei Liu, 2021. "An Online Parameter Estimation Using Current Injection with Intelligent Current-Loop Control for IPMSM Drives," Energies, MDPI, vol. 14(23), pages 1-21, December.

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