Torque Ripple Suppression of Switched Reluctance Motor with Reference Torque Online Correction
Benqin Jing, Xuanju Dang, Zheng Liu, Jianbo Ji
Abstract
High torque ripple dramatically affects the switched reluctance motor (SRM) application. To reduce the torque ripple, a reference torque neural network (RTNN) is proposed to adjust the reference torque online. Firstly, the RTNN is built on the torque sharing function (TSF) method. Furthermore, the RTNN is designed as a single-input and -output network. As the periodic relationship between the torque ripple and the rotor angle, the rotor angle constitutes the central node parameter of the implicit function in RTNN. Therefore, one-step adjustment of the RTNN can perform well at restraining reference torque. Lastly, the torque error is used to adjust the parameters of RTNN to reduce the torque ripple. In the MATLAB environment, through the simulation comparison with fuzzy torque and PD current compensation method, the effectiveness of RTNN at torque ripple suppression is proven with different loads and speeds.