Artificial intelligent based approaches of estimating of torque for multi-teeth per pole switched reluctance motor

This paper presents the derivation of artificial intelligence based models for estimation of torque of 24:22 configuration multi-teeth per pole switched reluctance motor. These developed fuzzy logic and neuro-fuzzy torque models are derived from suitable measured data sets of torque which are then tested in 1MIATLAB environment. Error analysis is also performed to determine the average percentage error of each type of artificial intelligent model. The analysis revealed that the accuracy and precision of the simulation results demonstrates that the fuzzy and neuro-fuzzy approaches are suitable for use in accurate predicting of torque of 24:22 configuration switched reluctance motor.