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Combined Optimal Torque Feedforward and Modal Current Feedback Control for Low Inductance PM Motors

Author

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  • Roland Kasper

    (Chair of Mechatronics, Otto von Guericke University, 39106 Magdeburg, Germany)

  • Dmytro Golovakha

    (Chair of Mechatronics, Otto von Guericke University, 39106 Magdeburg, Germany)

Abstract

Small sized electric motors providing high specific torque and power are required for many mobile applications. Air gap windings technology allows to create innovative lightweight and high-power electric motors that show low phase inductances. Low inductance leads to a small motor time constant, which enables fast current and torque control, but requires a high switching frequency and short sampling time to keep current ripples and losses in an acceptable range. This paper proposes an optimal torque feedforward control method, minimizing either torque ripples or motor losses, combined with a very robust and computation-efficient modal current feedback control. Compared to well-known control methods based on the Clarke-Park Transformations, the proposed strategy reduces torque ripples and motor losses significantly and offers a very fast implementation on standard microcontrollers with high robustness, e.g., against measurement errors of rotor angle. To verify the accuracy of the proposed control method, an experimental setup was used including a wheel hub motor built with a slotless air gap winding of low inductance, a standard microcontroller and GaN (Gallium Nitride) Power Devices allowing for high PWM switching frequencies. The proposed control method was validated first by correlation of simulation and experimental results and second by comparison to conventional field-oriented control.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:23:p:6184-:d:450556
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    References listed on IDEAS

    as
    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. Xinmin Li & Guokai Jiang & Wei Chen & Tingna Shi & Guozheng Zhang & Qiang Geng, 2019. "Commutation Torque Ripple Suppression Strategy of Brushless DC Motor Considering Back Electromotive Force Variation," Energies, MDPI, vol. 12(10), pages 1-14, May.
    3. Bo Tan & Zhiguang Hua & Lu Zhang & Chun Fang, 2017. "A New Approach of Minimizing Commutation Torque Ripple for BLDCM," Energies, MDPI, vol. 10(11), pages 1-13, October.
    4. 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.
    5. Qiang Song & Yiting Li & Chao Jia, 2018. "A Novel Direct Torque Control Method Based on Asymmetric Boundary Layer Sliding Mode Control for PMSM," Energies, MDPI, vol. 11(3), pages 1-15, March.
    6. Kamran Zeb & Waqar U. Din & Muhammad Adil Khan & Ayesha Khan & Umair Younas & Tiago Davi Curi Busarello & Hee Je Kim, 2018. "Dynamic Simulations of Adaptive Design Approaches to Control the Speed of an Induction Machine Considering Parameter Uncertainties and External Perturbations," Energies, MDPI, vol. 11(9), pages 1-25, September.
    7. Keun-Young Yoon & Soo-Whang Baek, 2019. "Robust Design Optimization with Penalty Function for Electric Oil Pumps with BLDC Motors," Energies, MDPI, vol. 12(1), pages 1-14, January.
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    Cited by:

    1. Tian-Hua Liu, 2021. "Design and Control of Electrical Motor Drives," Energies, MDPI, vol. 14(22), pages 1-3, November.

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