IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i2p167-d88957.html
   My bibliography  Save this article

Adaptive Torque Estimation for an IPMSM with Cross-Coupling and Parameter Variations

Author

Listed:
  • Dooyoung Yang

    (Center, Dongwoon Anatech Co. Ltd., Seoul 05029, Korea)

  • Hyungsoo Mok

    (Department of Electrical Engineering, Konkuk University, Seoul 05029, Korea)

  • Jusuk Lee

    (Department of Energy Mechanical Engineering, Gyeonggi College of Science and Technology, Gyeonggi 15073, Korea)

  • Soohee Han

    (Department of Creative IT Engineering, Phohang University of Science and Technology, Gyeongbuk 37673, Korea)

Abstract

This paper presents a new adaptive torque estimation algorithm for an interior permanent magnet synchronous motor (IPMSM) with parameter variations and cross-coupling between d- and q-axis dynamics. All cross-coupled, time-varying, or uncertain terms that are not part of the nominal flux equations are included in two equivalent mutual inductances, which are described using the equivalent d- and q-axis back electromotive forces (EMFs). The proposed algorithm estimates the equivalent d- and q-axis back EMFs in a recursive and stability-guaranteed manner, in order to compute the equivalent mutual inductances between the d- and q-axes. Then, it provides a more accurate and adaptive torque equation by adding the correction terms obtained from the computed equivalent mutual inductances. Simulations and experiments demonstrate that torque estimation errors are remarkably reduced by capturing and compensating for the inherent cross-coupling effects and parameter variations adaptively, using the proposed algorithm.

Suggested Citation

  • Dooyoung Yang & Hyungsoo Mok & Jusuk Lee & Soohee Han, 2017. "Adaptive Torque Estimation for an IPMSM with Cross-Coupling and Parameter Variations," Energies, MDPI, vol. 10(2), pages 1-13, January.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:2:p:167-:d:88957
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/2/167/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/2/167/
    Download Restriction: no
    ---><---

    Citations

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


    Cited by:

    1. Faa-Jeng Lin & Yi-Hung Liao & Jyun-Ru Lin & Wei-Ting Lin, 2021. "Interior Permanent Magnet Synchronous Motor Drive System with Machine Learning-Based Maximum Torque per Ampere and Flux-Weakening Control," Energies, MDPI, vol. 14(2), pages 1-24, January.
    2. Guan-Ren Chen & Shih-Chin Yang & Yu-Liang Hsu & Kang Li, 2017. "Position and Speed Estimation of Permanent Magnet Machine Sensorless Drive at High Speed Using an Improved Phase-Locked Loop," Energies, MDPI, vol. 10(10), pages 1-17, 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:10:y:2017:i:2:p:167-:d:88957. 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.