IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i11p1780-d117685.html
   My bibliography  Save this article

Sliding Mode and Neural Network Control of Sensorless PMSM Controlled System for Power Consumption and Performance Improvement

Author

Listed:
  • Ming-Shyan Wang

    (Department of Electrical Engineering, Southern Taiwan University of Science and Technology, 1, Nan-Tai St., Yung Kang District, Tainan City 710, Taiwan)

  • Tse-Ming Tsai

    (Department of Electrical Engineering, Southern Taiwan University of Science and Technology, 1, Nan-Tai St., Yung Kang District, Tainan City 710, Taiwan)

Abstract

This paper deals with the design of sliding mode control and neural network compensation for a sensorless permanent magnet synchronous motor (PMSM) controlled system that is able to improve both power consumption and speed response performance. The position sensor of PMSM is unreliable in harsh environments. Therefore, the sensorless control technique is widely proposed in industry. A sliding mode observer can estimate the rotor angle and has the robustness to load disturbance and parameter variations. However, the sliding mode observer is not conducive to standstill and low speed conditions because the amplitude of the back EMF is almost zero. As a result, this paper combines an iterative sliding mode observer (ISMO) and neural networks (NNs) as an angle compensator to improve the above problems. A dsPIC30F6010A-based PMSM sensorless drive system is implemented to validate the proposed algorithm. The simulation and experimental results prove its effectiveness.

Suggested Citation

  • Ming-Shyan Wang & Tse-Ming Tsai, 2017. "Sliding Mode and Neural Network Control of Sensorless PMSM Controlled System for Power Consumption and Performance Improvement," Energies, MDPI, vol. 10(11), pages 1-15, November.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:11:p:1780-:d:117685
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/11/1780/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/11/1780/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Manuel Schimmack & Eduardo E. Feistauer & Sergio T. Amancio-Filho & Paolo Mercorelli, 2017. "Hysteresis Analysis and Control of a Metal-Polymer Hybrid Soft Actuator," Energies, MDPI, vol. 10(4), pages 1-20, April.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Jyun-You Chen & Shih-Chin Yang & Kai-Hsiang Tu, 2018. "Comparative Evaluation of a Permanent Magnet Machine Saliency-Based Drive with Sine-Wave and Square-Wave Voltage Injection," Energies, MDPI, vol. 11(9), pages 1-15, August.
    2. 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.
    3. Karol Kyslan & Viktor Petro & Peter Bober & Viktor Šlapák & František Ďurovský & Mateusz Dybkowski & Matúš Hric, 2022. "A Comparative Study and Optimization of Switching Functions for Sliding-Mode Observer in Sensorless Control of PMSM," Energies, MDPI, vol. 15(7), pages 1-17, April.
    4. Omer Cihan Kivanc & Salih Baris Ozturk, 2019. "Low-Cost Position Sensorless Speed Control of PMSM Drive Using Four-Switch Inverter," Energies, MDPI, vol. 12(4), pages 1-24, February.
    5. Christian Aldrete-Maldonado & Ramon Ramirez-Villalobos & Luis N. Coria & Corina Plata-Ante, 2023. "Sensorless Scheme for Permanent-Magnet Synchronous Motors Susceptible to Time-Varying Load Torques," Mathematics, MDPI, vol. 11(14), pages 1-20, July.
    6. 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.
    7. Chunlei Wang & Dongxing Cao, 2020. "New Sensorless Speed Control of a Hybrid Stepper Motor Based on Fuzzy Sliding Mode Observer," Energies, MDPI, vol. 13(18), pages 1-19, September.
    8. Jongwon Choi & Kwanghee Nam, 2018. "Wound Synchronous Machine Sensorless Control Based on Signal Injection into the Rotor Winding," Energies, MDPI, vol. 11(12), pages 1-20, November.
    9. 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.
    10. 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.
    11. Xiaofei Zhang & Hongbin Ma, 2019. "Data-Driven Model-Free Adaptive Control Based on Error Minimized Regularized Online Sequential Extreme Learning Machine," Energies, MDPI, vol. 12(17), pages 1-17, August.
    12. 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.
    13. Shuo Chen & Xiao Zhang & Xiang Wu & Guojun Tan & Xianchao Chen, 2019. "Sensorless Control for IPMSM Based on Adaptive Super-Twisting Sliding-Mode Observer and Improved Phase-Locked Loop," Energies, MDPI, vol. 12(7), pages 1-19, March.

    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. Thang Nguyen Trong & Minh Nguyen Duc, 2017. "Sliding Surface in Consensus Problem of Multi-Agent Rigid Manipulators with Neural Network Controller," Energies, MDPI, vol. 10(12), pages 1-15, December.

    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:11:p:1780-:d:117685. 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.