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Sliding Mode Observer with Adaptive Parameter Estimation for Sensorless Control of IPMSM

Author

Listed:
  • Yubo Liu

    (College of Electrical and Information, Northeast Agricultural University, Harbin 150030, China)

  • Junlong Fang

    (College of Electrical and Information, Northeast Agricultural University, Harbin 150030, China)

  • Kezhu Tan

    (College of Electrical and Information, Northeast Agricultural University, Harbin 150030, China)

  • Boyan Huang

    (College of Electrical and Information, Northeast Agricultural University, Harbin 150030, China)

  • Wenshuai He

    (College of Electrical and Information, Northeast Agricultural University, Harbin 150030, China)

Abstract

To improve the observation accuracy and robustness of the sensorless control of an interior permanent magnet synchronous motor (IPMSM), a sliding mode observer based on the super twisting algorithm (STA-SMO) with adaptive parameters estimation control is proposed, as parameter mismatches are considered. First, the conventional sliding mode observer (CSMO) is analyzed. The conventional exponential approach law produces a large chattering phenomenon in the back EMF estimation, which causes a large observation error when filtering the chattering through the low-pass filter. Second, a high-order approach law of the super twisting algorithm is introduced to observe the rotor position and speed estimation, which uses the integral function to eliminate the chattering of the sliding mode. Third, an adaptive parameter estimation control (APEC) is presented to enhance the observation accuracy caused by parameter mismatches; the motor parameter adaptive law of the APEC is designed by Lyapunov’s stability law. Finally, the proposed method not only reduces both the chattering and the low-pass filter, but it also enhances accuracy and robustness against parameter mismatches, as discussed through simulations and experiments.

Suggested Citation

  • Yubo Liu & Junlong Fang & Kezhu Tan & Boyan Huang & Wenshuai He, 2020. "Sliding Mode Observer with Adaptive Parameter Estimation for Sensorless Control of IPMSM," Energies, MDPI, vol. 13(22), pages 1-18, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:22:p:5991-:d:446132
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    References listed on IDEAS

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    1. Cheng Liu & Guowei Cai & Jiwei Gao & Deyou Yang, 2017. "Design of Nonlinear Robust Damping Controller for Power Oscillations Suppressing Based on Backstepping-Fractional Order Sliding Mode," Energies, MDPI, vol. 10(5), pages 1-23, May.
    2. Mingcheng Lyu & Gongping Wu & Derong Luo & Fei Rong & Shoudao Huang, 2019. "Robust Nonlinear Predictive Current Control Techniques for PMSM," Energies, MDPI, vol. 12(3), pages 1-19, January.
    3. Gang Wang & Chenghui Zhou & Yu Yu & Xiaoping Liu, 2019. "Adaptive Sliding Mode Trajectory Tracking Control for WMR Considering Skidding and Slipping via Extended State Observer," Energies, MDPI, vol. 12(17), pages 1-16, August.
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    Cited by:

    1. Xiaolei Cai & Qixuan Wang & Yucheng Wang & Li Zhang, 2023. "Research on a Variable-Leakage-Flux Permanent Magnet Motor Control System Based on an Adaptive Tracking Estimator," Energies, MDPI, vol. 16(2), pages 1-16, January.
    2. Claudiu-Ionel Nicola & Marcel Nicola, 2023. "Improved Performance for PMSM Sensorless Control Based on the LADRC Controller, ESO-Type Observer, DO-Type Observer, and RL-TD3 Agent," Mathematics, MDPI, vol. 11(15), pages 1-25, July.
    3. Alessandro Benevieri & Lorenzo Carbone & Simone Cosso & Krishneel Kumar & Mario Marchesoni & Massimiliano Passalacqua & Luis Vaccaro, 2022. "Surface Permanent Magnet Synchronous Motors’ Passive Sensorless Control: A Review," Energies, MDPI, vol. 15(20), pages 1-26, October.
    4. 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.
    5. Thyago Estrabis & Gabriel Gentil & Raymundo Cordero, 2021. "Development of a Resolver-to-Digital Converter Based on Second-Order Difference Generalized Predictive Control," Energies, MDPI, vol. 14(2), pages 1-22, January.
    6. 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.
    7. Weidong Feng & Jing Bai & Zhiqiang Zhang & Jing Zhang, 2022. "A Composite Variable Structure PI Controller for Sensorless Speed Control Systems of IPMSM," Energies, MDPI, vol. 15(21), pages 1-18, November.
    8. 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.
    9. Artun Sel & Bilgehan Sel & Umit Coskun & Cosku Kasnakoglu, 2022. "SOS-Based Nonlinear Observer Design for Simultaneous State and Disturbance Estimation Designed for a PMSM Model," Sustainability, MDPI, vol. 14(17), pages 1-12, August.
    10. Yang Cao & Jian Guo, 2022. "Sensorless Control of High-Speed Motors Subject to Iron Loss," Energies, MDPI, vol. 15(20), pages 1-14, October.
    11. Pawel Latosinski & Andrzej Bartoszewicz, 2023. "Sliding Mode Controllers in Energy Systems and Other Applications," Energies, MDPI, vol. 16(3), pages 1-4, January.
    12. Paweł Latosiński & Andrzej Bartoszewicz, 2021. "Zero-Width Quasi-Sliding Mode Band in the Presence of Non-Matched Uncertainties," Energies, MDPI, vol. 14(11), pages 1-16, May.
    13. Habib Benbouhenni & Nicu Bizon, 2021. "Third-Order Sliding Mode Applied to the Direct Field-Oriented Control of the Asynchronous Generator for Variable-Speed Contra-Rotating Wind Turbine Generation Systems," Energies, MDPI, vol. 14(18), pages 1-20, September.

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