TY - GEN
T1 - ANN-Based Space Vector PWM Modulation for Permanent-Magnet Synchronous Motors
AU - Huang, Zhen
AU - Gong, Jiawei
AU - Wang, Chao
AU - Wang, Weiping
AU - Jia, Shaofeng
AU - Huang, Kunjie
AU - Xia, Yonghong
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - 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 %.
AB - 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 %.
UR - https://www.scopus.com/pages/publications/105017006607
U2 - 10.1109/PEDS63958.2025.11144770
DO - 10.1109/PEDS63958.2025.11144770
M3 - 会议稿件
AN - SCOPUS:105017006607
T3 - Proceedings of the International Conference on Power Electronics and Drive Systems
BT - IEEE Power Electronics and Drive Systems, PEDS 2025
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE International Conference on Power Electronics and Drive Systems, PEDS 2025
Y2 - 21 July 2025 through 24 July 2025
ER -