In recent reviews of vibrations in switched reluctance motors (SRMs) are processed to estimate the performances of the motors and limited with the acoustic noise and vibration. In this paper, the motor transient vibrations are predicted the response while sudden occurrences load changes or braking and sudden vibration.  The model of the system is developed by the proposed EnAMDF (Enhanced Average Magnitude Difference Function) using GRNN and details of torque, normal force versus speed and flux. The accuracy of the system is tested and verified by artificial neural networks. An adaptive enhanced mechanism based on GRNN is proposed to tune the gain PI controller and find the vibration and load of torque. The estimation and reduction of the SRMs vibration is simulated the magnetic circuit using the MATLAB/SIMULINK Environment. The results reveal the forces of transient are abundant harmonics and improve the possibilities of proposed system from acoustic noise point of view and vibration.

Keywords: GRNN; Transient; SRMs; Electromagnetic force; Vibrations; PI Controller