IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i11p4491-d1162505.html
   My bibliography  Save this article

High-Precision Acquisition Method of Position Signal of Permanent Magnet Direct Drive Servo Motor at Low Speed

Author

Listed:
  • Deli Zhang

    (College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

  • Zhaopeng Dong

    (College of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

  • Feifei Bu

    (College of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

  • Zijie Gu

    (College of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

  • Zitao Guo

    (College of Automation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

Abstract

This paper studies a method for high-precision acquisition of position signals for permanent magnet direct drive servo motors at low speed. First of all, the problem of poor position feedback accuracy and sensor feedback delay in the low-speed operation of the permanent magnet direct drive servo motor is analyzed. Secondly, through analysis and simulation, it is found that the interpolation method can play a certain role in compensating the rotor position signal. However, when the speed is close to 0, the output signal of the sensor will fluctuate in a short time, which will affect the speed control accuracy. Therefore, this paper uses the observer method to achieve high-precision acquisition of the position signal of the permanent magnet direct drive servo motor at low speed. The observer method adopts the idea of combining the system model and closed-loop control. Additionally, it makes full use of the parameter information of the motor system. The control performance of the motor can be better guaranteed through the design of the observer parameters and the accuracy of the rotor position estimation result has been greatly improved. Finally, an experimental platform for permanent magnet direct drive servo motors is built, and the rotor position signal acquisition method based on the observer method is verified to have good performance through simulation and experiments. Not only the accuracy of the rotor position estimation result is improved, but also the motor control performance is improved, realizing the stable operation of the permanent magnet direct drive servo motor at low speed.

Suggested Citation

  • Deli Zhang & Zhaopeng Dong & Feifei Bu & Zijie Gu & Zitao Guo, 2023. "High-Precision Acquisition Method of Position Signal of Permanent Magnet Direct Drive Servo Motor at Low Speed," Energies, MDPI, vol. 16(11), pages 1-15, June.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:11:p:4491-:d:1162505
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/11/4491/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/11/4491/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhang, Jiusi & Li, Xiang & Tian, Jilun & Jiang, Yuchen & Luo, Hao & Yin, Shen, 2023. "A variational local weighted deep sub-domain adaptation network for remaining useful life prediction facing cross-domain condition," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    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. Hyewon Lee & Izaz Raouf & Jinwoo Song & Heung Soo Kim & Soobum Lee, 2023. "Prognostics and Health Management of the Robotic Servo-Motor under Variable Operating Conditions," Mathematics, MDPI, vol. 11(2), pages 1-17, January.
    2. Hao, Zhaojun & Di Maio, Francesco & Zio, Enrico, 2023. "A sequential decision problem formulation and deep reinforcement learning solution of the optimization of O&M of cyber-physical energy systems (CPESs) for reliable and safe power production and supply," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    3. Zhujun Wang & Qin Su & Bi Wang & Jie Wang, 2023. "Improving Lithium-Ion Battery Supply Chain Information Security by User Behavior Monitoring Algorithm Incorporated in Cloud Enterprise Resource Planning," Sustainability, MDPI, vol. 15(4), pages 1-14, February.
    4. Ding, Peng & Zhao, Xiaoli & Shao, Haidong & Jia, Minping, 2023. "Machinery cross domain degradation prognostics considering compound domain shifts," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    5. Xiong, Jiawei & Zhou, Jian & Ma, Yizhong & Zhang, Fengxia & Lin, Chenglong, 2023. "Adaptive deep learning-based remaining useful life prediction framework for systems with multiple failure patterns," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    6. Alfarizi, Muhammad Gibran & Ustolin, Federico & Vatn, Jørn & Yin, Shen & Paltrinieri, Nicola, 2023. "Towards accident prevention on liquid hydrogen: A data-driven approach for releases prediction," Reliability Engineering and System Safety, Elsevier, vol. 236(C).
    7. Chen, Dingliang & Cai, Wei & Yu, Hangjun & Wu, Fei & Qin, Yi, 2023. "A novel transfer gear life prediction method by the cross-condition health indicator and nested hierarchical binary-valued network," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    8. Zhang, Zhiyao & Chen, Xiaohui & Zio, Enrico & Li, Longxiao, 2023. "Multi-task learning boosted predictions of the remaining useful life of aero-engines under scenarios of working-condition shift," Reliability Engineering and System Safety, Elsevier, vol. 237(C).

    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:11:p:4491-:d:1162505. 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.