Fuzzy Logic Controller Based Direct Torque Controller for a Switched Reluctance Motor Drive System

Abstract

This paper proposes a new control approach for torque ripple minimization of a Direct Torque
Controlled switched reluctance motor drive using Neural Network controller. It is still a very important problem
to determine vectors voltage space initially in the conventional DTC for optimizing the torque error. The
conventional control technique adopted for torque control uses hysteresis controller where the response of the
torque and its ripple are very sluggish at different operating condition. Hence this paper focuses on improving the torque and flux response by using the combination of Intelligent Controller with Direct Torque control technique which results in better speed regulation and reduced torque ripple even under non liner conditions.
The Intelligent controller gives high control over motor torque and speed, reduces rise time as well as overshoot.
The neural network controller based Direct torque control is simulated with MATLAB/SIMULINK.
The observed results shows the torque ripple minimization in switched reluctance motor can guarantee that the speed settling time is comparatively minimum as well as it flux and torque response is superior to conventional
PWM control. The efficiency of the drive mainly in terms of speed and torque ripple control is analyzed with new set of computational algorithm.