An efficient technique for torque ripple minimization with permanent magnet stepper motor [articol]

Abstract

In this paper presented an efficient technique is used for decreasing the torque ripple of the PMSM. The efficient technique is on the basis of the hybridization of ANN and fuzzy inference system that is called as ANFIS technique. The ANFIS technique is highly efficient in nonlinear systems because of the fact that once properly trained they can interpolate and extrapolate the random information with high accuracy. The main objective of the projected method is to reduce the torque ripples with the help of the regulating parameters, namely torque and speed. Initially the PMSM parameters are restrained and controlling the input parameters of the PMSM such as voltage and current. The torque and rotor angle of the PMSM is restrained for controlling the torque ripple and regulating the speed of the PMSM. From the measured parameters the error signal is considered from the actual and reference value of the rotor angle and torque of the PMSM. On the basis of the error signal the projected method is produced the control pulses, which is provided to VSC for supply the voltage and current signal to PMSM. The presented torque ripple minimization method is applied in MATLAB/Simulink working platform and the performances is assessed and compared with some available methods such as neural networks and fuzzy controller.