Sensorless Control of Switched Reluctance Motor Based on Improved Particle Filter

To enhance robustness and achieve more precise rotor position estimation in switched reluctance motors (SRMs), this article proposes an improved sensorless control method based on a particle filter observer. First, the unscented Kalman filter was chosen as the observer to replace the traditional particle filter, and the latest observation information was used to calculate the mean and variance. Second, to solve the particle degradation problem of the particle filtering algorithm and prevent falling into local optima, random resampling is used for weight calculation. The proposed method will have a more accurate parameter basis to improve the accuracy of position estimation, thereby optimizing the poor tracking performance when dealing with continuous changes. The performance of the proposed method was evaluated through experiments under different working conditions. The results show that the position estimation error based on the improved particle filter observer is within 1.25 ${{}^{\circ}}$ , demonstrating the effectiveness and good dynamic performance of the proposed method.