Neural control and fault simulation of 6/4 switched reluctance motor

This paper presents fault simulation and neural network based optimal speed controller with good robustness properties for a 6/4 switched reluctance motor. The neuro controller is trained by the back propagation algorithm considering the current reference as the output, the error and its derivative as the inputs as like fuzzy logic controller. The performance of the proposed controller is compared with fuzzy logic controller, a classical proportional plus integral controller and the faults are simulated using Matlab simulink. The simulation result is presented to demonstrate remarkable performance of the proposed controller for the switched reluctance motor.