ANN-Based Space Vector PWM Modulation for Permanent-Magnet Synchronous Motors - Huang, Zhen; Gong, Jiawei; Wang, Chao; Wang, Weiping; Jia, Shaofeng; Huang, Kunjie; Xia, Yonghong (International Conference on Power Electronics and Drive Systems (Online), 2025)
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 %.