IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i21p7180-d670293.html
   My bibliography  Save this article

State-Space Approach for SPMSM Sensorless Passive Algorithm Tuning

Author

Listed:
  • Lorenzo Carbone

    (Department of Electrical, Electronic, Tlc Engineering and Naval Architecture (DITEN), University of Genova, Via all’Opera Pia 11a, 16145 Genova, Italy)

  • Simone Cosso

    (Department of Electrical, Electronic, Tlc Engineering and Naval Architecture (DITEN), University of Genova, Via all’Opera Pia 11a, 16145 Genova, Italy)

  • Mario Marchesoni

    (Department of Electrical, Electronic, Tlc Engineering and Naval Architecture (DITEN), University of Genova, Via all’Opera Pia 11a, 16145 Genova, Italy)

  • Massimiliano Passalacqua

    (Department of Electrical, Electronic, Tlc Engineering and Naval Architecture (DITEN), University of Genova, Via all’Opera Pia 11a, 16145 Genova, Italy)

  • Luis Vaccaro

    (Department of Electrical, Electronic, Tlc Engineering and Naval Architecture (DITEN), University of Genova, Via all’Opera Pia 11a, 16145 Genova, Italy)

Abstract

Sensorless algorithms for Permanent Magnet Synchronous Motors (PMSM) have achieved increasing interest in the technical literature over the last few years. They can be divided into active methods and passive methods: the first inject high-frequency signals exploiting rotor anisotropy, whereas the second are based on observers. Recently, a sensorless control based on a rotor flux observer has been presented in the technical literature, which gives very accurate results in terms of rotor position estimation and robustness. In this paper, the aforementioned observer is considered and a procedure for choosing stabilizing gains of the observer is proposed. The contribution of the paper is three-fold: the mathematical modelling of the rotor flux observer, the methodology for the definition of the observer gains, and the presentation of the experimental results.

Suggested Citation

  • Lorenzo Carbone & Simone Cosso & Mario Marchesoni & Massimiliano Passalacqua & Luis Vaccaro, 2021. "State-Space Approach for SPMSM Sensorless Passive Algorithm Tuning," Energies, MDPI, vol. 14(21), pages 1-11, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:7180-:d:670293
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/21/7180/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/21/7180/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Justas Dilys & Voitech Stankevič & Krzysztof Łuksza, 2021. "Implementation of Extended Kalman Filter with Optimized Execution Time for Sensorless Control of a PMSM Using ARM Cortex-M3 Microcontroller," Energies, MDPI, vol. 14(12), pages 1-16, June.
    2. Danyang Bao & Huiming Wu & Ruiqi Wang & Fei Zhao & Xuewei Pan, 2020. "Full-Order Sliding Mode Observer Based on Synchronous Frequency Tracking Filter for High-Speed Interior PMSM Sensorless Drives," Energies, MDPI, vol. 13(24), pages 1-19, December.
    3. Jaime Pando-Acedo & Enrique Romero-Cadaval & Maria Isabel Milanes-Montero & Fermin Barrero-Gonzalez, 2020. "Improvements on a Sensorless Scheme for a Surface-Mounted Permanent Magnet Synchronous Motor Using Very Low Voltage Injection," Energies, MDPI, vol. 13(11), pages 1-17, May.
    4. Michal Gierczynski & Lech M. Grzesiak, 2021. "Comparative Analysis of the Steady-State Model Including Non-Linear Flux Linkage Surfaces and the Simplified Linearized Model when Applied to a Highly-Saturated Permanent Magnet Synchronous Machine—Ev," Energies, MDPI, vol. 14(9), pages 1-20, April.
    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. 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.
    2. 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.
    3. Konrad Urbanski & Dariusz Janiszewski, 2021. "Position Estimation at Zero Speed for PMSMs Using Artificial Neural Networks," Energies, MDPI, vol. 14(23), pages 1-17, December.
    4. Pawel Latosinski & Andrzej Bartoszewicz, 2023. "Sliding Mode Controllers in Energy Systems and Other Applications," Energies, MDPI, vol. 16(3), pages 1-4, January.
    5. Yang Liu & Jin Zhao & Quan Yin, 2021. "Model-Based Predictive Rotor Field-Oriented Angle Compensation for Induction Machine Drives," Energies, MDPI, vol. 14(8), pages 1-13, April.
    6. 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.
    7. 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.
    8. Jongwon Choi, 2021. "Regression Model-Based Flux Observer for IPMSM Sensorless Control with Wide Speed Range," Energies, MDPI, vol. 14(19), pages 1-18, October.
    9. Alessandro Benevieri & Lorenzo Carbone & Simone Cosso & Krishneel Kumar & Mario Marchesoni & Massimiliano Passalacqua & Luis Vaccaro, 2022. "Surface Permanent Magnet Synchronous Motors’ Passive Sensorless Control: A Review," Energies, MDPI, vol. 15(20), pages 1-26, 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:14:y:2021:i:21:p:7180-:d:670293. 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.