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Robust Speed Control of PMSM Using Sliding Mode Control (SMC)—A Review

Author

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  • Fardila Mohd Zaihidee

    (Power Electronics and Renewable Energy Research Laboratory (PEARL), Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
    Faculty of Technical and Vocational, Sultan Idris Education University, Tanjong Malim 35900, Perak, Malaysia)

  • Saad Mekhilef

    (Power Electronics and Renewable Energy Research Laboratory (PEARL), Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
    Center of Research Excellence in Renewable Energy and Power Systems, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Marizan Mubin

    (Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

Abstract

Permanent magnet synchronous motors (PMSMs) are known as highly efficient motors and are slowly replacing induction motors in diverse industries. PMSM systems are nonlinear and consist of time-varying parameters with high-order complex dynamics. High performance applications of PMSMs require their speed controllers to provide a fast response, precise tracking, small overshoot and strong disturbance rejection ability. Sliding mode control (SMC) is well known as a robust control method for systems with parameter variations and external disturbances. This paper investigates the current status of implementation of sliding mode control speed control of PMSMs. Our aim is to highlight various designs of sliding surface and composite controller designs with SMC implementation, which purpose is to improve controller’s robustness and/or to reduce SMC chattering. SMC enhancement using fractional order sliding surface design is elaborated and verified by simulation results presented. Remarkable features as well as disadvantages of previous works are summarized. Ideas on possible future works are also discussed, which emphasize on current gaps in this area of research.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:9:p:1669-:d:227718
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    References listed on IDEAS

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    1. 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.
    2. Matraji, Imad & Laghrouche, Salah & Jemei, Samir & Wack, Maxime, 2013. "Robust control of the PEM fuel cell air-feed system via sub-optimal second order sliding mode," Applied Energy, Elsevier, vol. 104(C), pages 945-957.
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    Cited by:

    1. Fawaz W. Alsaade & Mohammed S. Al-zahrani, 2023. "A Novel Fault-Tolerant Super-Twisting Control Technique for Chaos Stabilization in Fractional-Order Arch MEMS Resonators," Mathematics, MDPI, vol. 11(10), pages 1-18, May.
    2. Irfan Sami & Shafaat Ullah & Zahoor Ali & Nasim Ullah & Jong-Suk Ro, 2020. "A Super Twisting Fractional Order Terminal Sliding Mode Control for DFIG-Based Wind Energy Conversion System," Energies, MDPI, vol. 13(9), pages 1-20, May.
    3. Mingfei Huang & Yongting Deng & Hongwen Li & Meng Shao & Jing Liu, 2021. "Integrated Uncertainty/Disturbance Suppression Based on Improved Adaptive Sliding Mode Controller for PMSM Drives," Energies, MDPI, vol. 14(20), pages 1-19, October.
    4. Aleš Hace, 2019. "The Advanced Control Approach based on SMC Design for the High-Fidelity Haptic Power Lever of a Small Hybrid Electric Aircraft," Energies, MDPI, vol. 12(15), pages 1-31, August.
    5. Zhenjie Gong & Xin Ba & Chengning Zhang & Youguang Guo, 2022. "Robust Sliding Mode Control of the Permanent Magnet Synchronous Motor with an Improved Power Reaching Law," Energies, MDPI, vol. 15(5), pages 1-13, March.
    6. Muhammad Usama & Jaehong Kim, 2021. "Low-Speed Transient and Steady-State Performance Analysis of IPMSM for Nonlinear Speed Regulator with Effective Compensation Scheme," Energies, MDPI, vol. 14(20), pages 1-16, October.
    7. Marcel Nicola & Claudiu-Ionel Nicola & Dan Selișteanu, 2022. "Improvement of PMSM Sensorless Control Based on Synergetic and Sliding Mode Controllers Using a Reinforcement Learning Deep Deterministic Policy Gradient Agent," Energies, MDPI, vol. 15(6), pages 1-30, March.
    8. 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.
    9. Liqin Wu & Hao Chen & Tingyue Yu & Chengzhi Sun & Lin Wang & Xuerong Ye & Guofu Zhai, 2023. "Robust Design Optimization of the Cogging Torque for a PMSM Based on Manufacturing Uncertainties Analysis and Approximate Modeling," Energies, MDPI, vol. 16(2), pages 1-24, January.
    10. Jiachun Lin & Yuteng Zhao & Pan Zhang & Junjie Wang & Hao Su, 2021. "Research on Compound Sliding Mode Control of a Permanent Magnet Synchronous Motor in Electromechanical Actuators," Energies, MDPI, vol. 14(21), pages 1-17, November.
    11. Kifayat Ullah & Jaroslaw Guzinski & Adeel Feroz Mirza, 2022. "Critical Review on Robust Speed Control Techniques for Permanent Magnet Synchronous Motor (PMSM) Speed Regulation," Energies, MDPI, vol. 15(3), pages 1-13, February.
    12. Younes Zahraoui & Fardila M. Zaihidee & Mostefa Kermadi & Saad Mekhilef & Marizan Mubin & Jing Rui Tang & Ezrinda M. Zaihidee, 2023. "Fractional Order Sliding Mode Controller Based on Supervised Machine Learning Techniques for Speed Control of PMSM," Mathematics, MDPI, vol. 11(6), pages 1-21, March.
    13. Feng Jiang & Fan Yang & Songjun Sun & Kai Yang, 2022. "Improved Linear Active Disturbance Rejection Control for IPMSM Drives Considering Load Inertia Mismatch," Energies, MDPI, vol. 15(3), pages 1-22, February.
    14. Farya Golesorkhie & Fuwen Yang & Ljubo Vlacic & Geoff Tansley, 2020. "Field Oriented Control-Based Reduction of the Vibration and Power Consumption of a Blood Pump," Energies, MDPI, vol. 13(15), pages 1-18, July.
    15. Abhinandan Routray & Yiza Srikanth Reddy & Sung-ho Hur, 2023. "Predictive Control of a Wind Turbine Based on Neural Network-Based Wind Speed Estimation," Sustainability, MDPI, vol. 15(12), pages 1-22, June.
    16. Hassam Muazzam & Mohamad Khairi Ishak & Athar Hanif & Ali Arshad Uppal & AI Bhatti & Nor Ashidi Mat Isa, 2022. "Virtual Sensor Using a Super Twisting Algorithm Based Uniform Robust Exact Differentiator for Electric Vehicles," Energies, MDPI, vol. 15(5), pages 1-18, February.
    17. Yoon-Seong Lee & Kyoung-Min Choo & Won-Sang Jeong & Chang-Hee Lee & Junsin Yi & Chung-Yuen Won, 2023. "A Virtual Impedance-Based Flying Start Considering Transient Characteristics for Permanent Magnet Synchronous Machine Drive Systems," Energies, MDPI, vol. 16(3), pages 1-17, January.
    18. Btissam Majout & Houda El Alami & Hassna Salime & Nada Zine Laabidine & Youness El Mourabit & Saad Motahhir & Manale Bouderbala & Mohammed Karim & Badre Bossoufi, 2022. "A Review on Popular Control Applications in Wind Energy Conversion System Based on Permanent Magnet Generator PMSG," Energies, MDPI, vol. 15(17), pages 1-41, August.
    19. Marcin Kamiński & Krzysztof Szabat, 2021. "Adaptive Control Structure with Neural Data Processing Applied for Electrical Drive with Elastic Shaft," Energies, MDPI, vol. 14(12), pages 1-26, June.
    20. Mingyuan Hu & Hyeongki Ahn & Yoonuh Chung & Kwanho You, 2023. "Speed Regulation for PMSM with Super-Twisting Sliding-Mode Controller via Disturbance Observer," Mathematics, MDPI, vol. 11(7), pages 1-15, March.
    21. Younes Zahraoui & Fardila M. Zaihidee & Mostefa Kermadi & Saad Mekhilef & Ibrahim Alhamrouni & Mehdi Seyedmahmoudian & Alex Stojcevski, 2023. "Optimal Tuning of Fractional Order Sliding Mode Controller for PMSM Speed Using Neural Network with Reinforcement Learning," Energies, MDPI, vol. 16(11), pages 1-17, May.
    22. Cristian Napole & Oscar Barambones & Mohamed Derbeli & Isidro Calvo & Mohammed Yousri Silaa & Javier Velasco, 2021. "High-Performance Tracking for Piezoelectric Actuators Using Super-Twisting Algorithm Based on Artificial Neural Networks," Mathematics, MDPI, vol. 9(3), pages 1-20, January.

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