IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i3p657-d136414.html
   My bibliography  Save this article

A Novel Direct Torque Control Method Based on Asymmetric Boundary Layer Sliding Mode Control for PMSM

Author

Listed:
  • Qiang Song

    (Beijing Co-innovation Center for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China)

  • Yiting Li

    (Beijing Co-innovation Center for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China)

  • Chao Jia

    (Beijing Co-innovation Center for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China)

Abstract

A novel direct torque control (DTC) method based on sliding-mode-control (SMC) strategy is proposed for permanent magnet synchronous motor (PMSM) which is used in electric vehicles (EVs). In order to improve the dynamic response time and enhance the robustness performance against the external loading disturbances and motor parameter’s variation, a kind of SMC-based torque controller and speed controller are designed to regulate the torque angle increment and the speed respectively. The torque controller is designed based on a sliding mode controller with an asymmetric boundary layer to reduce the overshoot. Compared with other DTC methods based on space vector modulation (SVM) in the literature, the proposed DTC scheme adopts the asymmetric boundary layer SMC instead of the proportional-integral (PI) regulator. The simulation results have validated the effectiveness of the proposed SMC-based DTC method.

Suggested Citation

  • Qiang Song & Yiting Li & Chao Jia, 2018. "A Novel Direct Torque Control Method Based on Asymmetric Boundary Layer Sliding Mode Control for PMSM," Energies, MDPI, vol. 11(3), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:3:p:657-:d:136414
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/3/657/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/3/657/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Habib Benbouhenni & Nicu Bizon, 2021. "Improved Rotor Flux and Torque Control Based on the Third-Order Sliding Mode Scheme Applied to the Asynchronous Generator for the Single-Rotor Wind Turbine," Mathematics, MDPI, vol. 9(18), pages 1-16, September.
    2. Zhenjie Gong & Xin Ba & Chengning Zhang & Youguang Guo, 2022. "Robust Sliding Mode Control of the Permanent Magnet Synchronous Motor with an Improved Power Reaching Law," Energies, MDPI, vol. 15(5), pages 1-13, March.
    3. Yonghun Kim & Hyung-Tae Seo & Seok-Kyoon Kim & Kyung-Soo Kim, 2018. "A Robust Current Controller for Uncertain Permanent Magnet Synchronous Motors with a Performance Recovery Property for Electric Power Steering Applications," Energies, MDPI, vol. 11(5), pages 1-17, May.
    4. Zhanqing Zhou & Xin Gu & Zhiqiang Wang & Guozheng Zhang & Qiang Geng, 2019. "An Improved Torque Control Strategy of PMSM Drive Considering On-Line MTPA Operation," Energies, MDPI, vol. 12(15), pages 1-17, July.
    5. Roland Kasper & Dmytro Golovakha, 2020. "Combined Optimal Torque Feedforward and Modal Current Feedback Control for Low Inductance PM Motors," Energies, MDPI, vol. 13(23), pages 1-16, November.
    6. Rui Xiong & Suleiman M. Sharkh & Xi Zhang, 2018. "Research Progress on Electric and Intelligent Vehicles," Energies, MDPI, vol. 11(7), pages 1-5, July.
    7. Nikola Lopac & Neven Bulic & Niksa Vrkic, 2019. "Sliding Mode Observer-Based Load Angle Estimation for Salient-Pole Wound Rotor Synchronous Generators," Energies, MDPI, vol. 12(9), pages 1-22, April.
    8. Shun Li & Xinxiu Zhou, 2018. "Sensorless Energy Conservation Control for Permanent Magnet Synchronous Motors Based on a Novel Hybrid Observer Applied in Coal Conveyer Systems," Energies, MDPI, vol. 11(10), pages 1-23, September.
    9. Younes Zahraoui & Fardila M. Zaihidee & Mostefa Kermadi & Saad Mekhilef & Ibrahim Alhamrouni & Mehdi Seyedmahmoudian & Alex Stojcevski, 2023. "Optimal Tuning of Fractional Order Sliding Mode Controller for PMSM Speed Using Neural Network with Reinforcement Learning," Energies, MDPI, vol. 16(11), pages 1-17, May.

    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:11:y:2018:i:3:p:657-:d:136414. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.