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Realization of Intelligent Observer for Sensorless PMSM Drive Control

Author

Listed:
  • Dwi Sudarno Putra

    (Department of Electrical Engineering, Southern Taiwan University of Science and Technology, Tainan 710, Taiwan
    Department of Automotive Engineering, Universitas Negeri Padang, Padang 25132, Indonesia)

  • Seng-Chi Chen

    (Department of Electrical Engineering, Southern Taiwan University of Science and Technology, Tainan 710, Taiwan)

  • Hoai-Hung Khong

    (Faculty of Electrical and Electronics Engineering, Ho Chi Minh City University of Transport, Ho Chi Minh City 70000, Vietnam)

  • Chin-Feng Chang

    (Fukuta Electric and Machinery Co., Ltd., Taichung City 429, Taiwan)

Abstract

An observer is a crucial part of the sensorless control of a permanent magnet synchronous motor (PMSM). An observer, based on mathematical equations, depends on information regarding several parameters of the controlled motor. If the motor is replaced, then we need to know the motor parameter values and reset the observer’s parameters. This article discusses an intelligent observer that can be used for several motors with different parameters. The proposed intelligent observer was developed using machine learning methods. This observer’s core algorithm is a modified Jordan neural network. It processes I α , I β , v α , and v β to produce Sin θ and Cos θ values. It is combined with a phase-locked loop function to generate position and speed feedback information. The offline learning process is carried out using data acquired from the simulations of PMSM motors. This study used five PMSMs with different parameters, three as the learning reference sources and two as testing sources. The proposed intelligent observer was successfully used to control motors with different parameters in both simulation and experimental hardware. The average error in position estimated for the simulation was 0.0078 p.u and the error was 0.0100 p.u for the experimental realization.

Suggested Citation

  • Dwi Sudarno Putra & Seng-Chi Chen & Hoai-Hung Khong & Chin-Feng Chang, 2023. "Realization of Intelligent Observer for Sensorless PMSM Drive Control," Mathematics, MDPI, vol. 11(5), pages 1-20, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:5:p:1254-:d:1088197
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    References listed on IDEAS

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    1. Trancho, E. & Ibarra, E. & Arias, A. & Kortabarria, I. & Prieto, P. & Martínez de Alegría, I. & Andreu, J. & López, I., 2018. "Sensorless control strategy for light-duty EVs and efficiency loss evaluation of high frequency injection under standardized urban driving cycles," Applied Energy, Elsevier, vol. 224(C), pages 647-658.
    2. Pang, Zhihong & Niu, Fuxin & O’Neill, Zheng, 2020. "Solar radiation prediction using recurrent neural network and artificial neural network: A case study with comparisons," Renewable Energy, Elsevier, vol. 156(C), pages 279-289.
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    Cited by:

    1. Christian Aldrete-Maldonado & Ramon Ramirez-Villalobos & Luis N. Coria & Corina Plata-Ante, 2023. "Sensorless Scheme for Permanent-Magnet Synchronous Motors Susceptible to Time-Varying Load Torques," Mathematics, MDPI, vol. 11(14), pages 1-20, July.

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