FLC Based Speed Control of SR Motor with Neural Network Based Rotor-Angle Estimation

Switched reluctance motor (SRM) drives are used in various applications due to their simplicity, reliability and low cost schemes. It required rotor position feedback to operate and used for high concern in many applications. Thus, a major consideration is the robustness of position estimation schemes when sensorless SRM drive control is employed. The control of SRM using a tuned Fuzzy Logic Controller (FLC) along with neural estimator is done in this paper. The structure hierarchy and computational complexity of the controller is simplified by reducing the number of fuzzy sets in the function without losing the system performance. It uses the FLC for controlling the SRM speed. The FLC performs a PI-like control strategy, giving the current reference variation based on speed error and its change. In this work, we considered the speed variations with FLC and neural estimated rotor angle corresponding to load variables in response to changes in set points.