ANN-Based Space Vector PWM Modulation for Permanent-Magnet Synchronous Motors

This paper proposes an artificial neural network (ANN)-based space vector PWM (SVPWM) inverter controller for permanent-magnet synchronous motors (PMSM). Traditional SVPWM control methods involve complex computations and exhibit poor robustness to motor parameter variations and load disturbances, ma...

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Published in:International Conference on Power Electronics and Drive Systems (Online) pp. 1 - 5
Main Authors: Huang, Zhen, Gong, Jiawei, Wang, Chao, Wang, Weiping, Jia, Shaofeng, Huang, Kunjie, Xia, Yonghong
Format: Conference Proceeding
Language:English
Published: IEEE 21.07.2025
Subjects:
Aerospace electronics
Artificial neural networks
Backpropagation algorithms
Bayes methods
Computational efficiency
Motors
Robustness
Space vector pulse width modulation
Synchronous motors
Vectors
ISSN:2164-5264
Online Access:Get full text
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Abstract This paper proposes an artificial neural network (ANN)-based space vector PWM (SVPWM) inverter controller for permanent-magnet synchronous motors (PMSM). Traditional SVPWM control methods involve complex computations and exhibit poor robustness to motor parameter variations and load disturbances, making them inadequate for high-precision and high-dynamic-response applications. Due to its strong nonlinear mapping capability and adaptability, ANN can optimize SVPWM control strategies, enhancing system real-time performance and robustness. This study employs an ANN trained using the Bayesian regularization backpropagation algorithm and introduces a modular, low-complexity ANN-based SVPWM implementation scheme. Compared to conventional methods, the proposed approach reduces the online computational burden, improves efficiency, and is validated through simulations in the MATLAB/Simulink environment. The results demonstrate that ANN-based SVPWM control maintains high waveform quality across different modulation indices while reducing computational costs by approximately 10 % - 15 %.
AbstractList This paper proposes an artificial neural network (ANN)-based space vector PWM (SVPWM) inverter controller for permanent-magnet synchronous motors (PMSM). Traditional SVPWM control methods involve complex computations and exhibit poor robustness to motor parameter variations and load disturbances, making them inadequate for high-precision and high-dynamic-response applications. Due to its strong nonlinear mapping capability and adaptability, ANN can optimize SVPWM control strategies, enhancing system real-time performance and robustness. This study employs an ANN trained using the Bayesian regularization backpropagation algorithm and introduces a modular, low-complexity ANN-based SVPWM implementation scheme. Compared to conventional methods, the proposed approach reduces the online computational burden, improves efficiency, and is validated through simulations in the MATLAB/Simulink environment. The results demonstrate that ANN-based SVPWM control maintains high waveform quality across different modulation indices while reducing computational costs by approximately 10 % - 15 %.
Author Gong, Jiawei
Wang, Weiping
Wang, Chao
Huang, Kunjie
Xia, Yonghong
Jia, Shaofeng
Huang, Zhen
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  givenname: Yonghong
  surname: Xia
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  email: 090548@ncu.edu.cn
  organization: School of Information Engineering, Nanchang University
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Snippet This paper proposes an artificial neural network (ANN)-based space vector PWM (SVPWM) inverter controller for permanent-magnet synchronous motors (PMSM)....
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SubjectTerms Aerospace electronics
Artificial neural networks
Backpropagation algorithms
Bayes methods
Computational efficiency
Motors
Robustness
Space vector pulse width modulation
Synchronous motors
Vectors
Title ANN-Based Space Vector PWM Modulation for Permanent-Magnet Synchronous Motors
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