IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i17p3123-d902800.html
   My bibliography  Save this article

A Novel Nonsingular Terminal Sliding Mode Observer-Based Sensorless Control for Electrical Drive System

Author

Listed:
  • Yongjie Yang

    (College of Automation, Qingdao University, Qingdao 266071, China)

  • Xudong Liu

    (College of Automation, Qingdao University, Qingdao 266071, China
    Shandong Key Laboratory of Industrial Control Technology, Qingdao 266071, China)

Abstract

To improve the sensorless control performance of electrical drive systems, a nonsingular terminal sliding mode observer (NTSMO) and adaptive observer are proposed to solve the chattering and phase delay problems. Firstly, by defining a new nonsingular terminal sliding mode surface, the sliding mode observer based on the fast reaching law is designed to estimate the back electromotive force (EMF). The observer enhances the robustness and system performance eliminates the singularity and attenuates the chattering. Next, to obtain the accurate back-EMF signal, an adaptive observer is designed instead of a traditional low-pass filter to filter out the harmonics. The adaptive observer can avoid the phase delay problem and further improve the signal observation accuracy. Then, the rotor position and speed information are accurately tracked. The proposed method is applied to the speed control system of a permanent magnet synchronous motor (PMSM), and the effectiveness and feasibility of the proposed sliding mode observer are demonstrated by the experiment.

Suggested Citation

  • Yongjie Yang & Xudong Liu, 2022. "A Novel Nonsingular Terminal Sliding Mode Observer-Based Sensorless Control for Electrical Drive System," Mathematics, MDPI, vol. 10(17), pages 1-16, August.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:17:p:3123-:d:902800
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/17/3123/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/17/3123/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Marcel Nicola & Claudiu-Ionel Nicola & Dan Selișteanu, 2022. "Improvement of PMSM Sensorless Control Based on Synergetic and Sliding Mode Controllers Using a Reinforcement Learning Deep Deterministic Policy Gradient Agent," Energies, MDPI, vol. 15(6), pages 1-30, March.
    2. Bowen Xu & Jien Ma & Qiyi Wu & Lin Qiu & Xing Liu & Chao Luo & Youtong Fang, 2022. "Sensorless Control Strategy of Novel Axially Magnetized Vernier Permanent-Magnet Machine," Energies, MDPI, vol. 15(15), pages 1-19, July.
    3. Anton Dianov & Alecksey Anuchin, 2021. "Design of Constraints for Seeking Maximum Torque per Ampere Techniques in an Interior Permanent Magnet Synchronous Motor Control," Mathematics, MDPI, vol. 9(21), pages 1-21, November.
    4. Yujiao Zhao & Haisheng Yu & Shixian Wang, 2021. "An Improved Super-Twisting High-Order Sliding Mode Observer for Sensorless Control of Permanent Magnet Synchronous Motor," Energies, MDPI, vol. 14(19), pages 1-18, September.
    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. Marcel Nicola & Claudiu-Ionel Nicola, 2022. "Improvement of Linear and Nonlinear Control for PMSM Using Computational Intelligence and Reinforcement Learning," Mathematics, MDPI, vol. 10(24), pages 1-34, December.
    2. Claudiu-Ionel Nicola & Marcel Nicola, 2023. "Improved Performance for PMSM Sensorless Control Based on the LADRC Controller, ESO-Type Observer, DO-Type Observer, and RL-TD3 Agent," Mathematics, MDPI, vol. 11(15), pages 1-25, July.
    3. Yanfei Cao & Shuxin Xiao & Zhichen Lin, 2022. "A Flying Restart Strategy for Position Sensorless PMSM Driven by Quasi-Z-Source Inverter," Energies, MDPI, vol. 15(9), pages 1-15, May.
    4. Vadim Kazakbaev & Aleksey Paramonov & Vladimir Dmitrievskii & Vladimir Prakht & Victor Goman, 2022. "Indirect Efficiency Measurement Method for Line-Start Permanent Magnet Synchronous Motors," Mathematics, MDPI, vol. 10(7), pages 1-14, March.
    5. Shuai Li & Ke Zhu & Liang Chen & Yao Yan & Qing Guo, 2022. "Variable Structure Disturbance Observer Based Dynamic Surface Control of Electrohydraulic Systems with Parametric Uncertainty," Energies, MDPI, vol. 15(5), pages 1-15, February.
    6. Di Liu & Junwei Cao & Mingshuang Liu, 2022. "Joint Optimization of Energy Storage Sharing and Demand Response in Microgrid Considering Multiple Uncertainties," Energies, MDPI, vol. 15(9), pages 1-20, April.
    7. Yoon-Seong Lee & Kyoung-Min Choo & Won-Sang Jeong & Chang-Hee Lee & Junsin Yi & Chung-Yuen Won, 2023. "A Virtual Impedance-Based Flying Start Considering Transient Characteristics for Permanent Magnet Synchronous Machine Drive Systems," Energies, MDPI, vol. 16(3), pages 1-17, January.

    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:jmathe:v:10:y:2022:i:17:p:3123-:d:902800. 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.