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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
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    References listed on IDEAS

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    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.
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