IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i13p2558-d245231.html
   My bibliography  Save this article

DSP Implementation of a Neural Network Vector Controller for IPM Motor Drives

Author

Listed:
  • Yang Sun

    (Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL 35401, USA)

  • Shuhui Li

    (Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL 35401, USA)

  • Malek Ramezani

    (Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL 35401, USA)

  • Bharat Balasubramanian

    (Center for Advanced Vehicle Technologies, The University of Alabama, Tuscaloosa, AL 35401, USA)

  • Bian Jin

    (Lab. of Networked Control Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China)

  • Yixiang Gao

    (Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL 35401, USA)

Abstract

This paper develops a neural network (NN) vector controller for an interior mounted permanent magnet (IPM) motor by using a Texas Instrument TMS320F28335 digital signal processor (DSP). The NN controller is developed based on the complete state-space equation of an IPM motor and it is trained to achieve optimal control according to approximate dynamic programming (ADP). A DSP-based NN control system is built for an IPM motor drives system, and a high efficient DSP program is developed to implement the NN control algorithm while considering the limited memory and computing capability of the TMS320F28335 DSP. The DSP-based NN controller is able to manage IPM motor control in linear, over, and six-step modulation regions to improve the efficiency of IPM drives and to allow for the full utilization of DC bus voltage with space-vector pulse-width modulation (SVPWM). The experiment results show that the proposed NN controller is able to operate with a sampling period of 0.1ms, even with limited DSP resources of up to 150 MHz cycle time, which is applicable in practical motor industrial implementations. The NN controller has demonstrated a better current and speed tracking performance than the conventional standard vector controller for IPM operation in both the linear and over-modulation regions.

Suggested Citation

  • Yang Sun & Shuhui Li & Malek Ramezani & Bharat Balasubramanian & Bian Jin & Yixiang Gao, 2019. "DSP Implementation of a Neural Network Vector Controller for IPM Motor Drives," Energies, MDPI, vol. 12(13), pages 1-17, July.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:13:p:2558-:d:245231
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/13/2558/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/12/13/2558/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Thanh Anh Huynh & Min-Fu Hsieh, 2018. "Performance Analysis of Permanent Magnet Motors for Electric Vehicles (EV) Traction Considering Driving Cycles," Energies, MDPI, vol. 11(6), pages 1-24, May.
    2. Myeong-Hwan Hwang & Jong-Ho Han & Dong-Hyun Kim & Hyun-Rok Cha, 2018. "Design and Analysis of Rotor Shapes for IPM Motors in EV Power Traction Platforms," Energies, MDPI, vol. 11(10), pages 1-12, 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. Łukasz Knypiński & Karol Pawełoszek & Yvonnick Le Menach, 2020. "Optimization of Low-Power Line-Start PM Motor Using Gray Wolf Metaheuristic Algorithm," Energies, MDPI, vol. 13(5), pages 1-11, March.
    2. Yi Du & Jiayan Zhou & Zhuofan He & Yandong Sun & Ming Kong, 2022. "A Dual-Harmonic Pole-Changing Motor with Split Permanent Magnet Pole," Energies, MDPI, vol. 15(20), pages 1-14, October.
    3. Yin-Hui Lee & Min-Fu Hsieh, 2022. "Swiveling Magnetization for Anisotropic Magnets for Variable Flux Spoke-Type Permanent Magnet Motor Applied to Electric Vehicles," Energies, MDPI, vol. 15(10), pages 1-20, May.
    4. Pavol Rafajdus & Valeria Hrabovcova & Pavel Lehocky & Pavol Makys & Filip Holub, 2018. "Effect of Saturation on Field Oriented Control of the New Designed Reluctance Synchronous Motor," Energies, MDPI, vol. 11(11), pages 1-10, November.
    5. Armagan Bozkurt & Ahmet Fevzi Baba & Yusuf Oner, 2021. "Design of Outer-Rotor Permanent-Magnet-Assisted Synchronous Reluctance Motor for Electric Vehicles," Energies, MDPI, vol. 14(13), pages 1-12, June.
    6. Peter Stumpf & Tamás Tóth-Katona, 2023. "Recent Achievements in the Control of Interior Permanent-Magnet Synchronous Machine Drives: A Comprehensive Overview of the State of the Art," Energies, MDPI, vol. 16(13), pages 1-46, July.
    7. Pedro P. C. Bhagubai & João G. Sarrico & João F. P. Fernandes & P. J. Costa Branco, 2020. "Design, Multi-Objective Optimization, and Prototyping of a 20 kW 8000 rpm Permanent Magnet Synchronous Motor for a Competition Electric Vehicle," Energies, MDPI, vol. 13(10), pages 1-24, May.
    8. Piotr Mynarek & Janusz Kołodziej & Adrian Młot & Marcin Kowol & Marian Łukaniszyn, 2021. "Influence of a Winding Short-Circuit Fault on Demagnetization Risk and Local Magnetic Forces in V-Shaped Interior PMSM with Distributed and Concentrated Winding," Energies, MDPI, vol. 14(16), pages 1-16, August.
    9. Minhyeok Lee & Yunkyung Hwang & Kwanghee Nam, 2021. "Torque Ripple Minimizing of Uniform Slot Machines with Delta Rotor via Subdomain Analysis," Energies, MDPI, vol. 14(21), pages 1-18, November.
    10. Edison Gundabattini & Arkadiusz Mystkowski & Adam Idzkowski & Raja Singh R. & Darius Gnanaraj Solomon, 2021. "Thermal Mapping of a High-Speed Electric Motor Used for Traction Applications and Analysis of Various Cooling Methods—A Review," Energies, MDPI, vol. 14(5), pages 1-32, March.
    11. Chao Wu & Jun Yang & Qi Li, 2020. "GPIO-Based Nonlinear Predictive Control for Flux-Weakening Current Control of the IPMSM Servo System," Energies, MDPI, vol. 13(7), pages 1-21, April.
    12. Marcin Jastrzębski & Jacek Kabziński, 2021. "Approximation of Permanent Magnet Motor Flux Distribution by Partially Informed Neural Networks," Energies, MDPI, vol. 14(18), pages 1-21, September.
    13. 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.
    14. Namala Narasimhulu & R. S. R. Krishnam Naidu & Przemysław Falkowski-Gilski & Parameshachari Bidare Divakarachari & Upendra Roy, 2022. "Energy Management for PV Powered Hybrid Storage System in Electric Vehicles Using Artificial Neural Network and Aquila Optimizer Algorithm," Energies, MDPI, vol. 15(22), pages 1-21, November.
    15. Catalin Petrea Ion & Marius Daniel Calin & Ioan Peter, 2023. "Design of a 3 kW PMSM with Super Premium Efficiency," Energies, MDPI, vol. 16(1), pages 1-11, January.
    16. Zeyang Fan & Hong Yi & Jian Xu & Kun Xie & Yue Qi & Sailin Ren & Hongdong Wang, 2021. "Performance Study and Optimization Design of High-Speed Amorphous Alloy Induction Motor," Energies, MDPI, vol. 14(9), pages 1-19, April.
    17. Huimin Li & Shoudao Huang & Derong Luo & Jian Gao & Peng Fan, 2018. "Dynamic DC-link Voltage Adjustment for Electric Vehicles Considering the Cross Saturation Effects," Energies, MDPI, vol. 11(8), pages 1-22, August.
    18. Felix Veeser & Tristan Braun & Lothar Kiltz & Johannes Reuter, 2021. "Nonlinear Modelling, Flatness-Based Current Control, and Torque Ripple Compensation for Interior Permanent Magnet Synchronous Machines," Energies, MDPI, vol. 14(6), pages 1-14, March.
    19. Giampaolo Buticchi & David Gerada & Luigi Alberti & Michael Galea & Pat Wheeler & Serhiy Bozhko & Sergei Peresada & He Zhang & Chengming Zhang & Chris Gerada, 2019. "Challenges of the Optimization of a High-Speed Induction Machine for Naval Applications," Energies, MDPI, vol. 12(12), pages 1-20, June.
    20. Pedram Asef & Ramon Bargallo & Andrew Lapthorn & Davide Tavernini & Lingyun Shao & Aldo Sorniotti, 2021. "Assessment of the Energy Consumption and Drivability Performance of an IPMSM-Driven Electric Vehicle Using Different Buried Magnet Arrangements," Energies, MDPI, vol. 14(5), pages 1-22, 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:12:y:2019:i:13:p:2558-:d:245231. 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.