IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i2p642-d726488.html
   My bibliography  Save this article

Automated QFT-Based PI Tuning for Speed Control of SynRM Drive with Analytical Selection of QFT Control Specifications

Author

Listed:
  • Rajesh Poola

    (Department of Life Science and Systems Engineering, Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Fukuoka 808-0196, Japan)

  • Tsuyoshi Hanamoto

    (Department of Life Science and Systems Engineering, Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Fukuoka 808-0196, Japan)

Abstract

The gains of PI controllers, used in the cascaded speed control of synchronous reluctance motors (SynRMs), are synthesized using quantitative feedback theory (QFT). A systematic design approach is employed to quantitatively determine the PI controller gains in terms of speed and current loops, using a mathematical model of the SynRM. Further, to make the QFT design a more transparent method, an analytical procedure using the frequency domain is attempted to design the QFT bounds as well as the initial search space of the optimization algorithm used in automatic loop shaping. The effectiveness of the proposed PI tuning method is verified with the extensive MATLAB/Simulink simulation environment. The results illustrate the supremacy of the proposed PI tuning method, in terms of control performance, over the conventional PI tuning method, using the analytical procedure.

Suggested Citation

  • Rajesh Poola & Tsuyoshi Hanamoto, 2022. "Automated QFT-Based PI Tuning for Speed Control of SynRM Drive with Analytical Selection of QFT Control Specifications," Energies, MDPI, vol. 15(2), pages 1-17, January.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:2:p:642-:d:726488
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/2/642/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/2/642/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yuanzhe Zhao & Linjie Ren & Zhiming Liao & Guobin Lin, 2021. "A Novel Model Predictive Direct Torque Control Method for Improving Steady-State Performance of the Synchronous Reluctance Motor," Energies, MDPI, vol. 14(8), pages 1-18, April.
    2. Victor Goman & Vladimir Prakht & Vadim Kazakbaev & Vladimir Dmitrievskii, 2021. "Comparative Study of Energy Consumption and CO 2 Emissions of Variable-Speed Electric Drives with Induction and Synchronous Reluctance Motors in Pump Units," Mathematics, MDPI, vol. 9(21), pages 1-16, October.
    3. Nezih Gokhan Ozcelik & Ugur Emre Dogru & Murat Imeryuz & Lale T. Ergene, 2019. "Synchronous Reluctance Motor vs. Induction Motor at Low-Power Industrial Applications: Design and Comparison," Energies, MDPI, vol. 12(11), pages 1-20, June.
    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. Paweł Idziak & Krzysztof Kowalski, 2021. "Analysis of Selected Operating States of the Line Start Synchronous Reluctance Motor Using the Finite Element Method," Energies, MDPI, vol. 14(20), pages 1-18, October.
    2. Yuanzhe Zhao & Linjie Ren & Zhiming Liao & Guobin Lin, 2021. "A Novel Model Predictive Direct Torque Control Method for Improving Steady-State Performance of the Synchronous Reluctance Motor," Energies, MDPI, vol. 14(8), pages 1-18, April.
    3. Giovanni Bucci & Fabrizio Ciancetta & Edoardo Fiorucci & Simone Mari & Maria Anna Segreto, 2019. "The Measurement of Additional Losses in Induction Motors: Discussion about the Actually Achievable Uncertainty," Energies, MDPI, vol. 13(1), pages 1-13, December.
    4. Chiweta E. Abunike & Udochukwu B. Akuru & Ogbonnaya I. Okoro & Chukwuemeka C. Awah, 2023. "Sizing, Modeling, and Performance Comparison of Squirrel-Cage Induction and Wound-Field Flux Switching Motors," Mathematics, MDPI, vol. 11(16), pages 1-24, August.
    5. Hyunwoo Kim & Yeji Park & Seung-Taek Oh & Hyungkwan Jang & Sung-Hong Won & Yon-Do Chun & Ju Lee, 2020. "A Study on the Rotor Design of Line Start Synchronous Reluctance Motor for IE4 Efficiency and Improving Power Factor," Energies, MDPI, vol. 13(21), pages 1-15, November.
    6. Duc-Kien Ngo & Min-Fu Hsieh, 2019. "Performance Analysis of Synchronous Reluctance Motor with Limited Amount of Permanent Magnet," Energies, MDPI, vol. 12(18), pages 1-20, September.
    7. 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.
    8. Hyunwoo Kim & Yeji Park & Huai-Cong Liu & Pil-Wan Han & Ju Lee, 2020. "Study on Line-Start Permanent Magnet Assistance Synchronous Reluctance Motor for Improving Efficiency and Power Factor," Energies, MDPI, vol. 13(2), pages 1-15, January.
    9. Victor Goman & Vladimir Prakht & Vadim Kazakbaev & Vladimir Dmitrievskii, 2021. "Comparative Study of Energy Consumption and CO 2 Emissions of Variable-Speed Electric Drives with Induction and Synchronous Reluctance Motors in Pump Units," Mathematics, MDPI, vol. 9(21), pages 1-16, October.
    10. Linjie Ren & Guobin Lin & Yuanzhe Zhao & Zhiming Liao, 2021. "Smart Collaborative Performance-Induced Parameter Identification Algorithms for Synchronous Reluctance Machine Magnetic Model," Sustainability, MDPI, vol. 13(8), pages 1-14, April.
    11. Florin Pop-Pîgleşan & Adrian-Cornel Pop & Claudia Marțiş, 2021. "Synchronous Reluctance Machines for Automotive Cooling Fan Systems: Numerical and Experimental Study of Different Slot-Pole Combinations and Winding Types," Energies, MDPI, vol. 14(2), pages 1-28, 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:jeners:v:15:y:2022:i:2:p:642-:d:726488. 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.