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Dual-Vector Predictive Torque Control of Permanent Magnet Synchronous Motors Based on a Candidate Vector Table

Author

Listed:
  • Yan Xu

    (School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China)

  • Tingna Shi

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Yan Yan

    (School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China)

  • Xin Gu

    (Tianjin Engineering Center of Electric Machine System Design and Control, Tianjin Polytechnic University, Tianjin 300387, China)

Abstract

In order to reduce the torque ripple of permanent magnet synchronous motors (PMSMs), this paper proposes a dual-vector predictive torque control strategy based on a candidate vector table. The main feature of this strategy is that two vectors are acted in a control period to form a vector combination, and the vector combination can be either an effective-zero combination or an effective-effective combination. In the process of establishing the vector combinations, the switching frequency is also taken into account, therefore avoiding a high switching frequency, while effectively reducing the motor torque ripple. The candidate vector table is constructed offline, and three sets of candidate vectors and their duty cycles can be determined by looking up the table. Then the cost function is used to screen the action vectors from the three sets candidate vectors, so the two vectors acted in one control period and their duty cycles can be obtained simultaneously. Finally, the feasibility and effectiveness of the proposed method are verified on a 5.2 kW two-level inverter-fed PMSM drive system.

Suggested Citation

  • Yan Xu & Tingna Shi & Yan Yan & Xin Gu, 2019. "Dual-Vector Predictive Torque Control of Permanent Magnet Synchronous Motors Based on a Candidate Vector Table," Energies, MDPI, vol. 12(1), pages 1-15, January.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:1:p:163-:d:194872
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    References listed on IDEAS

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    1. Lisi Tian & Jin Zhao & Jiajiang Sun, 2016. "Sensorless Control of Interior Permanent Magnet Synchronous Motor in Low-Speed Region Using Novel Adaptive Filter," Energies, MDPI, vol. 9(12), pages 1-18, December.
    2. Jian Li & Xiaoyan Huang & Feng Niu & Chaojie You & Lijian Wu & Youtong Fang, 2018. "Prediction Error Analysis of Finite-Control-Set Model Predictive Current Control for IPMSMs," Energies, MDPI, vol. 11(8), pages 1-16, August.
    3. Xianglin Li & K. T. Chau & Yubin Wang, 2016. "Modeling of a Field-Modulated Permanent-Magnet Machine," Energies, MDPI, vol. 9(12), pages 1-15, December.
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    Cited by:

    1. Jemma J. Makrygiorgou & Antonio T. Alexandridis, 2019. "Power Electronic Control Design for Stable EV Motor and Battery Operation during a Route," Energies, MDPI, vol. 12(10), pages 1-21, May.
    2. Kang Wang & Ruituo Huai & Zhihao Yu & Xiaoyang Zhang & Fengjuan Li & Luwei Zhang, 2019. "Comparison Study of Induction Motor Models Considering Iron Loss for Electric Drives," Energies, MDPI, vol. 12(3), pages 1-13, February.
    3. Zhanqing Zhou & Xin Gu & Zhiqiang Wang & Guozheng Zhang & Qiang Geng, 2019. "An Improved Torque Control Strategy of PMSM Drive Considering On-Line MTPA Operation," Energies, MDPI, vol. 12(15), pages 1-17, July.
    4. Hassan Mohammadi Pirouz & Amin Hajizadeh, 2020. "A Highly Reliable Propulsion System with Onboard Uninterruptible Power Supply for Train Application: Topology and Control," Sustainability, MDPI, vol. 12(10), pages 1-30, May.
    5. Daniel R. Ramirez & Cristina Martin & Agnieszka Kowal G. & Manuel R. Arahal, 2019. "Min-Max Predictive Control of a Five-Phase Induction Machine," Energies, MDPI, vol. 12(19), pages 1-9, September.

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