Optimized torque control of switched reluctance motor at all operational regimes using neural network

Torque in a switched reluctance motor (SRM) at low speed is controlled by PWM chopping of current. The controller provides the appropriate reference current, the phase turn-on, and the turn-off angles based on the torque demand and the motor speed. While the torque at high speed is controlled by the single pulse operation of the current. The controllable parameters at high speeds are the phase turn-on and the turn-off angles. The controller is responsible for providing the appropriate turn-on and the turn-off angles. The complexity in the control of SRM arises from the fact that the SRM operation is highly nonlinear. By design SRM operates in the saturation region in almost all operational points. This results in the nonlinearity of the SRM magnetic field. To find the appropriate control parameters at low as well as at high speeds, an accurate nonlinear model of the SRM is, therefore, needed. In this paper such a dynamic model of SRM is developed. Simulation and experimental results are presented to demonstrate the effectiveness of the proposed control scheme.