IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i22p7482-d1275838.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/22/7482/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/22/7482/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xiaojun Ren & Zhikai Chen & Rui Du & Ming Feng, 2024. "Loss and Thermal Analysis of a High-Power-Density Permanent Magnet Starter/Generator," Energies, MDPI, vol. 17(20), pages 1-15, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:22:p:7482-:d:1275838. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.