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An Improved Finite-Control-Set Model Predictive Current Control for IPMSM under Model Parameter Mismatches

Author

Listed:
  • Zehao Lyu

    (School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221000, China)

  • Xiang Wu

    (School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221000, China)

  • Jie Gao

    (School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221000, China)

  • Guojun Tan

    (School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221000, China)

Abstract

The control performance of the finite control set model predictive current control (FCS-MPCC) for the interior permanent magnet synchronous machine (IPMSM) depends on the accuracy of the mathematical model. A novel robust model predictive current control method based on error compensation is proposed in order to reduce the parameter sensitivity and improve the current control robustness. In this method, the equivalent parameters are obtained from the known voltage and current information at the past time and the error between the predicted current and the actual current at the present time, which is utilized in the two-step prediction process to compensate the parameter mismatch error. Finally, the optimal voltage vector is selected by the cost function. The proposed method is compared with the traditional model predictive current control method through experiments. The experimental results show the effectiveness of the proposed method.

Suggested Citation

  • Zehao Lyu & Xiang Wu & Jie Gao & Guojun Tan, 2021. "An Improved Finite-Control-Set Model Predictive Current Control for IPMSM under Model Parameter Mismatches," Energies, MDPI, vol. 14(19), pages 1-13, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6342-:d:649854
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    References listed on IDEAS

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    1. 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.
    2. Ahmed Nasr & Chunyang Gu & Serhiy Bozhko & Chris Gerada, 2020. "Performance Enhancement of Direct Torque-Controlled Permanent Magnet Synchronous Motor with a Flexible Switching Table," Energies, MDPI, vol. 13(8), pages 1-15, April.
    3. Mingcheng Lyu & Gongping Wu & Derong Luo & Fei Rong & Shoudao Huang, 2019. "Robust Nonlinear Predictive Current Control Techniques for PMSM," Energies, MDPI, vol. 12(3), pages 1-19, January.
    4. Ting Yang & Takahiro Kawaguchi & Seiji Hashimoto & Wei Jiang, 2020. "Switching Sequence Model Predictive Direct Torque Control of IPMSMs for EVs in Switch Open-Circuit Fault-Tolerant Mode," Energies, MDPI, vol. 13(21), pages 1-15, October.
    5. Lihui Wang & Guojun Tan & Jie Meng, 2019. "Research on Model Predictive Control of IPMSM Based on Adaline Neural Network Parameter Identification," Energies, MDPI, vol. 12(24), pages 1-16, December.
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    Cited by:

    1. Yuzhe Zhang & Xiaodong Liu & Haitao Li & Zhenbin Zhang, 2023. "A Model Independent Predictive Control of PMSG Wind Turbine Systems with a New Mechanism to Update Variables," Energies, MDPI, vol. 16(9), pages 1-15, April.
    2. Songklod Sriprang & Nitchamon Poonnoy & Babak Nahid-Mobarakeh & Noureddine Takorabet & Nicu Bizon & Pongsiri Mungporn & Phatiphat Thounthong, 2022. "Design, Modeling, and Model-Free Control of Permanent Magnet-Assisted Synchronous Reluctance Motor for e-Vehicle Applications," Sustainability, MDPI, vol. 14(9), pages 1-21, April.

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