Online modeling for switched reluctance motors using adaptive network based fuzzy inference system

A novel online modeling scheme for the switched reluctance motor (SRM) using an adaptive network based fuzzy inference system (ANFIS) is proposed in this paper. The parameters of ANFIS are tuned through online supervised learning to improve the accuracy of the model, so it is robust toward parameter variations in the motor or any system disturbances. To eliminate the expensive torque sensor used in this model scheme, the improved online modeling scheme is proposed with a torque estimator replaces the sensor. It is suitable to apply the improved online modeling scheme to the torque control system design, which can be verified by the simulation of SRM torque control.