IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i10p2769-d552879.html
   My bibliography  Save this article

Stator Fixed Deadbeat Predictive Torque and Flux Control of a PMSM Drive with Modulated Duty Cycle

Author

Listed:
  • Omar Sandre Hernandez

    (Cátedras CONACYT, CITIS, AACyE, ICBI, Autonomous University of Hidalgo State, Pachuca 42184, Hidalgo, Mexico
    CITIS, AACyE, ICBI, Autonomous University of Hidalgo State, Pachuca 42184, Hidalgo, Mexico)

  • Jorge S. Cervantes-Rojas

    (Cátedras CONACYT, CITIS, AACyE, ICBI, Autonomous University of Hidalgo State, Pachuca 42184, Hidalgo, Mexico
    CITIS, AACyE, ICBI, Autonomous University of Hidalgo State, Pachuca 42184, Hidalgo, Mexico)

  • Jesus P. Ordaz Oliver

    (CITIS, AACyE, ICBI, Autonomous University of Hidalgo State, Pachuca 42184, Hidalgo, Mexico)

  • Carlos Cuvas Castillo

    (CITIS, AACyE, ICBI, Autonomous University of Hidalgo State, Pachuca 42184, Hidalgo, Mexico)

Abstract

Conventional deadbeat control strategies for permanent magnet synchronous machines (PMSMs) are commonly developed reference frames, however, coupling dynamics affect the performance drive, and rotational transformations are required for the synthesis of the final voltage vector (VV). To improve robustness against parameter variations and to directly synthesize the reference voltage vector, in this paper a deadbeat predictive torque and flux control for a PMSM is presented. The proposed controller is developed in the stationary reference frame ( α − β ). First, the reference VV is obtained from a predictive deadbeat controller. Then, the reference VV is applied to the power inverter by the combination of two voltage vectors. A duty cycle optimization is employed to calculate the required time for the application of each voltage vector. Experimental results based on an FPGA and a comparison of the conventional and the proposed deadbeat controller are presented to validate the proposed methodology.

Suggested Citation

  • Omar Sandre Hernandez & Jorge S. Cervantes-Rojas & Jesus P. Ordaz Oliver & Carlos Cuvas Castillo, 2021. "Stator Fixed Deadbeat Predictive Torque and Flux Control of a PMSM Drive with Modulated Duty Cycle," Energies, MDPI, vol. 14(10), pages 1-15, May.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:10:p:2769-:d:552879
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/10/2769/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/10/2769/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jae Suk Lee, 2018. "Stability Analysis of Deadbeat-Direct Torque and Flux Control for Permanent Magnet Synchronous Motor Drives with Respect to Parameter Variations," Energies, MDPI, vol. 11(8), pages 1-18, August.
    2. GuangQing Bao & WuGang Qi & Ting He, 2020. "Direct Torque Control of PMSM with Modified Finite Set Model Predictive Control," Energies, MDPI, vol. 13(1), pages 1-16, January.
    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. Feng Cai & Ke Li & Xiaodong Sun & Minkai Wu, 2021. "Air-Gap Flux Oriented Vector Control Based on Reduced-Order Flux Observer for EESM," Energies, MDPI, vol. 14(18), pages 1-19, September.

    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. Bowei Zou & Yougui Guo & Xi Xiao & Bowen Yang & Xiao Wang & Mingzhang Shi & Yulin Tu, 2020. "Performance Improvement of Matrix Converter Direct Torque Control System," Energies, MDPI, vol. 13(12), pages 1-17, June.
    2. Jie Chen & Jiajun Wang & Bo Yan, 2022. "Simulation Research on Deadbeat Direct Torque and Flux Control of Permanent Magnet Synchronous Motor," Energies, MDPI, vol. 15(9), pages 1-15, April.
    3. Karol Wróbel & Piotr Serkies & Krzysztof Szabat, 2020. "Model Predictive Base Direct Speed Control of Induction Motor Drive—Continuous and Finite Set Approaches," Energies, MDPI, vol. 13(5), pages 1-15, March.
    4. Sandra Eriksson, 2019. "Permanent Magnet Synchronous Machines," Energies, MDPI, vol. 12(14), pages 1-5, July.
    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. Jaime A. Rohten & David N. Dewar & Pericle Zanchetta & Andrea Formentini & Javier A. Muñoz & Carlos R. Baier & José J. Silva, 2021. "Multivariable Deadbeat Control of Power Electronics Converters with Fast Dynamic Response and Fixed Switching Frequency," Energies, MDPI, vol. 14(2), pages 1-16, January.
    7. Chunyan Li & Fei Guo & Baoquan Kou & Tao Meng, 2021. "Research on the Non-Magnetic Conductor of a PMSM Based on the Principle of Variable Exciting Magnetic Reluctance," Energies, MDPI, vol. 14(2), pages 1-29, January.
    8. Habib Benbouhenni & Nicu Bizon, 2021. "Improved Rotor Flux and Torque Control Based on the Third-Order Sliding Mode Scheme Applied to the Asynchronous Generator for the Single-Rotor Wind Turbine," Mathematics, MDPI, vol. 9(18), pages 1-16, September.
    9. Hao Yu & Jiajun Wang & Zhuangzhuang Xin, 2022. "Model Predictive Control for PMSM Based on Discrete Space Vector Modulation with RLS Parameter Identification," Energies, MDPI, vol. 15(11), pages 1-16, May.
    10. Pankaj Kumar & Yashwant Kashyap & Roystan Vijay Castelino & Anabalagan Karthikeyan & Manjunatha Sharma K. & Debabrata Karmakar & Panagiotis Kosmopoulos, 2023. "Laboratory-Scale Airborne Wind Energy Conversion Emulator Using OPAL-RT Real-Time Simulator," Energies, MDPI, vol. 16(19), pages 1-30, September.
    11. 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.
    12. 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.
    13. Karol Tucki, 2021. "A Computer Tool for Modelling CO 2 Emissions in Driving Cycles for Spark Ignition Engines Powered by Biofuels," Energies, MDPI, vol. 14(5), pages 1-33, March.

    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:10:p:2769-:d:552879. 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.