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Enhanced Dual–Vector Model Predictive Control for PMSM Drives Using the Optimal Vector Selection Principle

Author

Listed:
  • Zhen Huang

    (School of Information Engineering, Nanchang University, Nanchang 330031, China)

  • Qiang Wei

    (School of Information Engineering, Nanchang University, Nanchang 330031, China)

  • Xuechun Xiao

    (School of Information Engineering, Nanchang University, Nanchang 330031, China)

  • Yonghong Xia

    (School of Information Engineering, Nanchang University, Nanchang 330031, China)

  • Marco Rivera

    (Power Electronics, Machines and Control (PEMC) Research Group, Department of Electrical and Electronic Engineering, Faculty of Engineering, University of Nottingham, Lenton, Nottingham NG7 2GT, UK
    Laboratorio de Conversión de Energías y Electrónica de Potencia (LCEEP), Faculty of Engineering, Universidad de Talca, Merced 437, Curicó 3341717, Chile)

  • Patrick Wheeler

    (Power Electronics, Machines and Control (PEMC) Research Group, Department of Electrical and Electronic Engineering, Faculty of Engineering, University of Nottingham, Lenton, Nottingham NG7 2GT, UK)

Abstract

The Dual–Vector model predictive control (DV–MPC) method can improve the steady–state control performance of motor drives compared to using the single–vector method in one switching cycle. However, this performance enhancement generally increases the computational burden due to the exponential increase in the number of vector selections, lowering the system’s dynamic response. Alternatively, limiting the vector combinations will sacrifice system steady–state performance. To address this issue, this paper proposes an enhanced DV–MPC method that can determine the optimal vector combinations along with their duration time within minimized calculation times. Compared to the existing DV–MPC methods, the proposed enhanced technique can achieve excellent steady–state performance while maintaining a low computational burden. These benefits have been demonstrated in the results from a 2.5k rpm permanent magnet synchronous motor drive.

Suggested Citation

  • Zhen Huang & Qiang Wei & Xuechun Xiao & Yonghong Xia & Marco Rivera & Patrick Wheeler, 2023. "Enhanced Dual–Vector Model Predictive Control for PMSM Drives Using the Optimal Vector Selection Principle," Energies, MDPI, vol. 16(22), pages 1-14, November.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:22:p:7482-:d:1275838
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    References listed on IDEAS

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    1. Haoquan Zhang & Baoquan Kou & Lu Zhang, 2023. "Design and Analysis of a Stator Field Control Permanent Magnet Synchronous Starter–Generator System," Energies, MDPI, vol. 16(13), pages 1-20, July.
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