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

Yang Sun, Shuhui Li, Malek Ramezani, Bharat Balasubramanian, Bian Jin and Yixiang Gao
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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

Energies, 2019, vol. 12, issue 13, 1-17

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.

Keywords: permanent-magnet synchronous motor (PMSM); vector control; approximate dynamic programming (ADP); artificial neural network (ANN); digital signal processor (DSP) implementation; real-time control; linear and over-modulation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
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