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Continuous Control Set Model Predictive Control of a Switch Reluctance Drive Using Lookup Tables

Author

Listed:
  • Alecksey Anuchin

    (Department of Electric Drives, Moscow Power Engineering Institute, 111250 Moscow, Russia
    Faculty of Control Systems and Robotics, ITMO University, 197101 Saint Petersburg, Russia)

  • Galina L. Demidova

    (Faculty of Control Systems and Robotics, ITMO University, 197101 Saint Petersburg, Russia)

  • Chen Hao

    (School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China)

  • Alexandr Zharkov

    (Department of Electric Drives, Moscow Power Engineering Institute, 111250 Moscow, Russia)

  • Andrei Bogdanov

    (Faculty of Control Systems and Robotics, ITMO University, 197101 Saint Petersburg, Russia)

  • Václav Šmídl

    (Department of Adaptive Systems, Institute of Information Theory and Automation, CZ-182 00 Prague, Czech Republic)

Abstract

A problem of the switched reluctance drive is its natural torque pulsations, which are partially solved with finite control set model predictive control strategies. However, the continuous control set model predictive control, required for precise torque stabilization and predictable power converter behavior, needs sufficient computation resources, thus limiting its practical implementation. The proposed model predictive control strategy utilizes offline processing of the magnetization surface of the switched reluctance motor. This helps to obtain precalculated current references for each torque command and rotor angular position in the offline mode. In online mode, the model predictive control strategy implements the current commands using the magnetization surface for fast evaluation of the required voltage command for the power converter. The proposed strategy needs only two lookup table operations requiring very small computation time, making instant execution of the whole control system possible and thereby minimizing the control delay. The proposed solution was examined using a simulation model, which showed precise and rapid torque stabilization below rated speed.

Suggested Citation

  • Alecksey Anuchin & Galina L. Demidova & Chen Hao & Alexandr Zharkov & Andrei Bogdanov & Václav Šmídl, 2020. "Continuous Control Set Model Predictive Control of a Switch Reluctance Drive Using Lookup Tables," Energies, MDPI, vol. 13(13), pages 1-14, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:13:p:3317-:d:377831
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    References listed on IDEAS

    as
    1. Grace Firsta Lukman & Xuan Son Nguyen & Jin-Woo Ahn, 2020. "Design of a Low Torque Ripple Three-Phase SRM for Automotive Shift-by-Wire Actuator," Energies, MDPI, vol. 13(9), pages 1-13, May.
    2. Man Zhang & Imen Bahri & Xavier Mininger & Cristina Vlad & Hongqin Xie & Eric Berthelot, 2019. "A New Control Method for Vibration and Noise Suppression in Switched Reluctance Machines," Energies, MDPI, vol. 12(8), pages 1-16, April.
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    Cited by:

    1. Anouar Belahcen & Armando Pires & Vitor Fernão Pires, 2023. "Magnetic Material Modelling of Electrical Machines," Energies, MDPI, vol. 16(2), pages 1-3, January.
    2. Cherif Guerroudj & Yannis L. Karnavas & Jean-Frederic Charpentier & Ioannis D. Chasiotis & Lemnouer Bekhouche & Rachid Saou & Mohammed El-Hadi Zaïm, 2021. "Design Optimization of Outer Rotor Toothed Doubly Salient Permanent Magnet Generator Using Symbiotic Organisms Search Algorithm," Energies, MDPI, vol. 14(8), pages 1-25, April.
    3. Yuanfeng Lan & Yassine Benomar & Kritika Deepak & Ahmet Aksoz & Mohamed El Baghdadi & Emine Bostanci & Omar Hegazy, 2021. "Switched Reluctance Motors and Drive Systems for Electric Vehicle Powertrains: State of the Art Analysis and Future Trends," Energies, MDPI, vol. 14(8), pages 1-29, April.

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