MRAS-Based Sensorless Control of PMSM with BPN in Prediction Mode

摘要

In this paper, a novel model reference adaptive system (MRAS) observer based sensorless control of permanent magnet synchronous motor (PMSM) is proposed. This new speed observer uses the system model as the reference model, and the discrete system model with estimated rotor speed as the adaptive model to estimate the stator current, then, uses the gradient descent with an optimized proportional coefficient as the adaptive law to estimate the rotor speed. The adaptive model is a linear neural network which can be trained on line by means of back propagation. Moreover, the adaptive model is in prediction mode, which has a better performance compared with the simulation mode. The performance of the proposed method has been verified by Matlab/Simulink.