Design and Implementation of a Machine-Learning Observer for Sensorless PMSM Drive Control

Abstract

Information about rotor positions is critical when controlling a permanent-magnet synchronous motor (PMSM). This information can be gathered using a sensor or through an estimation without using a sensor. This article discusses a machine learning technique for estimating rotor positions. The proposed machine learning observer was constructed using a modified Elman neural network as the main algorithm. The network was trained offline with training data obtained from PMSM field-oriented control simulations and was tested using a validation data set. The PMSM control simulation results revealed that the rotor position estimated through machine learning was comparable with the simulated rotor position; the average error was 0.0127 per unit position. Furthermore, the machine learning model was implemented in an experimental PMSM-control hardware platform. Both the simulation and experimental results indicate that the proposed machine learning observer has an acceptable performance.

Keywords

• machine learning observer
• rotor position estimation
• sensorless motor control
• modified Elman neural network