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Full-Order Sliding Mode Observer Based on Synchronous Frequency Tracking Filter for High-Speed Interior PMSM Sensorless Drives

Author

Listed:
  • Danyang Bao

    (Shenzhen Polytechnic, Shenzhen 518055, China
    School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, China)

  • Huiming Wu

    (Shenzhen Polytechnic, Shenzhen 518055, China)

  • Ruiqi Wang

    (School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, China)

  • Fei Zhao

    (School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, China)

  • Xuewei Pan

    (School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518055, China)

Abstract

In the research of the high-speed sensorless control strategy of an interior permanent-magnet synchronous motor (IPMSM), considering the harmonic influence of inverter nonlinearity on traditional sliding mode observer method, a full-order sliding mode observer (SMO) method based on synchronous frequency tracking filtering is proposed. This method avoids the phase delay defects caused by the use of filters in traditional second-order SMO. Then, the observed extended electromotive force (EMF) signal is filtered using a synchronous frequency tracking (SFT) function. It tracks the changing stator current and filters out harmonics that are not part of the tracking signal to achieve static tracking of the stator current. Then, the rotor speed can be estimated by a Luenberger-based observer. Experimental results verify the effectiveness and feasibility of the proposed method.

Suggested Citation

  • Danyang Bao & Huiming Wu & Ruiqi Wang & Fei Zhao & Xuewei Pan, 2020. "Full-Order Sliding Mode Observer Based on Synchronous Frequency Tracking Filter for High-Speed Interior PMSM Sensorless Drives," Energies, MDPI, vol. 13(24), pages 1-19, December.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:24:p:6511-:d:459568
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    Citations

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    Cited by:

    1. Lorenzo Carbone & Simone Cosso & Mario Marchesoni & Massimiliano Passalacqua & Luis Vaccaro, 2021. "State-Space Approach for SPMSM Sensorless Passive Algorithm Tuning," Energies, MDPI, vol. 14(21), pages 1-11, November.
    2. Karol Kyslan & Viktor Petro & Peter Bober & Viktor Šlapák & František Ďurovský & Mateusz Dybkowski & Matúš Hric, 2022. "A Comparative Study and Optimization of Switching Functions for Sliding-Mode Observer in Sensorless Control of PMSM," Energies, MDPI, vol. 15(7), pages 1-17, April.
    3. Jongwon Choi, 2021. "Regression Model-Based Flux Observer for IPMSM Sensorless Control with Wide Speed Range," Energies, MDPI, vol. 14(19), pages 1-18, October.
    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. Yujiao Zhao & Haisheng Yu & Shixian Wang, 2021. "An Improved Super-Twisting High-Order Sliding Mode Observer for Sensorless Control of Permanent Magnet Synchronous Motor," Energies, MDPI, vol. 14(19), pages 1-18, September.
    6. Shuai Li & Ke Zhu & Liang Chen & Yao Yan & Qing Guo, 2022. "Variable Structure Disturbance Observer Based Dynamic Surface Control of Electrohydraulic Systems with Parametric Uncertainty," Energies, MDPI, vol. 15(5), pages 1-15, February.
    7. Pawel Latosinski & Andrzej Bartoszewicz, 2023. "Sliding Mode Controllers in Energy Systems and Other Applications," Energies, MDPI, vol. 16(3), pages 1-4, January.

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