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Prediction Error Analysis of Finite-Control-Set Model Predictive Current Control for IPMSMs

Author

Listed:
  • Jian Li

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

  • Xiaoyan Huang

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

  • Feng Niu

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
    State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China)

  • Chaojie You

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

  • Lijian Wu

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

  • Youtong Fang

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

Abstract

Finite-control-set model predictive current control (FCS-MPCC) has been widely investigated in the field of motor control. When the discrete motor prediction model is not obtained accurately, prediction error often occurs, which can result in improper determinations of optimal voltage vectors and can further affect the control performance of motor systems. However, papers evaluating the motor control performance employing FCS-MPCC rarely consider prediction error and its utilization to weaken the influence of inaccurate prediction model. This paper investigates in depth the prediction error caused by three influencing factors from the perspective of model accuracy—discretization method, prediction stepsize, and parameter mismatch. Firstly, the evaluation index, prediction error, is defined and its formulas considering the above three factors are derived based on interior permanent magnet synchronous motor (IPMSM). Then, the theoretical analysis of prediction error is provided. Finally, experimental results of an IPMSM drive system are presented to verify and complement the theoretical analysis. Both the theoretical analysis and experimental results fully elaborate the prediction error, which can offer practical guidelines for the evaluation and improvement of motor control performance, especially for FCS-MPCC in IPMSM applications.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:8:p:2051-:d:162456
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    References listed on IDEAS

    as
    1. Vijay Kumar Singh & Ravi Nath Tripathi & Tsuyoshi Hanamoto, 2018. "HIL Co-Simulation of Finite Set-Model Predictive Control Using FPGA for a Three-Phase VSI System," Energies, MDPI, vol. 11(4), pages 1-15, April.
    2. Huimin Li & Jian Gao & Shoudao Huang & Peng Fan, 2017. "A Novel Optimal Current Trajectory Control Strategy of IPMSM Considering the Cross Saturation Effects," Energies, MDPI, vol. 10(10), pages 1-16, September.
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    Citations

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    Cited by:

    1. Junlei Chen & Shuo Chen & Xiang Wu & Guojun Tan & Jianqi Hao, 2019. "A Super-Twisting Sliding-Mode Stator Flux Observer for Sensorless Direct Torque and Flux Control of IPMSM," Energies, MDPI, vol. 12(13), pages 1-17, July.
    2. Lynn Verkroost & Joachim Druant & Hendrik Vansompel & Frederik De Belie & Peter Sergeant, 2019. "Performance Degradation of Surface PMSMs with Demagnetization Defect under Predictive Current Control," Energies, MDPI, vol. 12(5), pages 1-20, February.
    3. Yufeng Zhang & Zihui Wu & Qi Yan & Nan Huang & Guanghui Du, 2022. "An Improved Model−Free Current Predictive Control of Permanent Magnet Synchronous Motor Based on High−Gain Disturbance Observer," Energies, MDPI, vol. 16(1), pages 1-16, December.
    4. 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.

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