Neural network based control for Switched Reluctance Motor drive

Abstract

Switched Reluctance Motors (SRMs) have evolved to represent interesting solutions for variable speed drive applications, due to their low cost and high dynamic performance capabilities. On the other hand, a number of less positive characteristics, such as their nonlinear behavior, and the existence of a significant torque ripple in the output also accompanied by audible noise, make the control problem associated with their operation a challenging task. These things limited their deployment in practical applications. The motivation of the present work is to simplify the control of SRM using Neural Network based control, to cut down the complexity and cost so that it can be accepted as a viable variable speed drive in general and a preferred drive for industrial and domestic applications This paper deals with the neural network based control for 8/6 pole Switched Reluctance Motor. Here, the neural network based controller, which is used to obtain the optimum turn on and turn off angles to minimize the torque ripple and speed ripple. The machine is modeled and simulated using Matlab / Simulink environment. The output response shows good dynamic behavior of the system.