Data-Driven Position Estimation and Sensorless Control of PMSM Drive in Low-Speed Region

This paper presents a data-driven method for position sensorless control of permanent magnet synchronous motors (PMSMs), specifically for improving performance in the low-speed region. Unlike previous data-driven approaches, this study introduces three key contributions. First, this study presents the design process of the neural network (NN)-based position estimator, with input feature selection through a grid search method. Second, an effective loss function and learning algorithm are proposed and utilized to improve the training performance of the NN-based estimator. Finally, a data augmentation method is introduced to efficiently construct the training dataset and reduce the data acquisition time. As a result, the proposed input features and training algorithm significantly improve the position estimation accuracy of the NN-based estimator when compared to existing data-driven methods. Furthermore, the developed model is implemented on the digital signal processor (DSP) to validate its real-time operation. The operable region in the four-quadrant operation is expanded by 49.80% in the low-speed region compared to the recently studied model-based method. Moreover, robustness under dynamic state operation is verified.