The Rotor Position Self-sensing of Permanent Magnet Synchronous Motor

Sensorless control of permanent magnet synchronous motor has been a hot research topic of motor control technology, which has the advantages of low cost and high reliability. In this paper, the rotor position estimation of permanent magnet synchronous motor sensorless control based on high-frequency signal injection were investigated. The paper presents that system noise due to the permanent magnet synchronous motor model simplification, parameter variation and measurement noise of high-frequency current signal sampling noise were not considered in the traditional linear observer. The kalman rotor position observer applied to estimate the rotor position of sensorless permanent magnet synchronous motor based on the high-frequency voltage signal injection is proposed. In order to yield the best estimation performance, a global optimized noise covariance estimation method based on Genetic Algorithms (GA) is firstly applied to kalman rotor position observer. The rotor position of the kalman rotor position observer is utilized to guide optimizing process in the Genetic Algorithms training, so the optimizing results are more suitable for the actual system. The simulation results verified the efficacy and accuracy of the Genetic Algorithms (GA) optimized kalman rotor position observer in position estimation.