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Multiple-Vector Model Predictive Control with Fuzzy Logic for PMSM Electric Drive Systems

Author

Listed:
  • Ibrahim Farouk Bouguenna

    (Institute for Electrical Engineering, University of Mascara, Mascara 29000, Algeria)

  • Ahmed Tahour

    (Higher School of Applied Sciences, Tlemcen 13000, Algeria)

  • Ralph Kennel

    (Institute for Electrical Drive Systems and Power Electronics (EAL), Technische Universität München (TUM), 80333 Munich, Germany)

  • Mohamed Abdelrahem

    (Institute for Electrical Drive Systems and Power Electronics (EAL), Technische Universität München (TUM), 80333 Munich, Germany
    Electrical Engineering Department, Faculty of Engineering, Assiut University, Assiut 71516, Egypt)

Abstract

This article presents a multiple-vector finite-control-set model predictive control (MV-FCS-MPC) scheme with fuzzy logic for permanent-magnet synchronous motors (PMSMs) used in electric drive systems. The proposed technique is based on discrete space vector modulation (DSVM). The converter’s real voltage vectors are utilized along with new virtual voltage vectors to form switching sequences for each sampling period in order to improve the steady-state performance. Furthermore, to obtain the reference voltage vector (VV) directly from the reference current and to reduce the calculation load of the proposed MV-FCS-MPC technique, a deadbeat function (DB) is added. Subsequently, the best real or virtual voltage vector to be applied in the next sampling instant is selected based on a certain cost function. Moreover, a fuzzy logic controller is employed in the outer loop for controlling the speed of the rotor. Accordingly, the dynamic response of the speed is improved and the difficulty of the proportional-integral (PI) controller tuning is avoided. The response of the suggested technique is verified by simulation results and compared with that of the conventional FCS-MPC.

Suggested Citation

  • Ibrahim Farouk Bouguenna & Ahmed Tahour & Ralph Kennel & Mohamed Abdelrahem, 2021. "Multiple-Vector Model Predictive Control with Fuzzy Logic for PMSM Electric Drive Systems," Energies, MDPI, vol. 14(6), pages 1-23, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:6:p:1727-:d:520883
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    References listed on IDEAS

    as
    1. Long Sheng & Dapeng Li & Yue Ji, 2018. "Two-Vector FCS-MPC for Permanent-Magnet Synchronous Motors Based on Duty Ratio Optimization," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-9, May.
    2. Bouguenna, Ibrahim Farouk & Azaiz, Ahmed & Tahour, Ahmed & Larbaoui, Ahmed, 2019. "Robust neuro-fuzzy sliding mode control with extended state observer for an electric drive system," Energy, Elsevier, vol. 169(C), pages 1054-1063.
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    Citations

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    Cited by:

    1. Muhammad Usama & Jaehong Kim, 2021. "Low-Speed Transient and Steady-State Performance Analysis of IPMSM for Nonlinear Speed Regulator with Effective Compensation Scheme," Energies, MDPI, vol. 14(20), pages 1-16, October.
    2. Mingmao Hu & Feng Yang & Yi Liu & Liang Wu, 2022. "Finite Control Set Model-Free Predictive Current Control of a Permanent Magnet Synchronous Motor," Energies, MDPI, vol. 15(3), pages 1-18, January.
    3. Zhiming Liao & Tianran Peng & Jia Liu & Tao Guo, 2023. "Multi-Adjustment Strategy for Phase Current Reconstruction of Permanent Magnet Synchronous Motors Based on Model Predictive Control," Energies, MDPI, vol. 16(15), pages 1-16, July.
    4. Paolo Mercorelli, 2022. "Model Predictive Control for Energy Optimization in Generators/Motors as Well as Converters and Inverters for Futuristic Integrated Power Networks," Energies, MDPI, vol. 15(16), pages 1-4, August.
    5. Khoudir Kakouche & Adel Oubelaid & Smail Mezani & Djamila Rekioua & Toufik Rekioua, 2023. "Different Control Techniques of Permanent Magnet Synchronous Motor with Fuzzy Logic for Electric Vehicles: Analysis, Modelling, and Comparison," Energies, MDPI, vol. 16(7), pages 1-28, March.

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