Artificial Intelligence-based control for torque ripple minimization in switched reluctance motor drives

In this paper, various intelligent controllers such as Fuzzy Logic Controller (FLC) and Adaptive Neuro Fuzzy Inference System (ANFIS)-based current compensating techniques are employed for minimizing the torque ripples in switched reluctance motor. FLC and ANFIS controllers are tuned using MATLAB Toolbox. For the purpose of comparison, the performance of conventional Proportional-Integral (PI) controller is also considered. The statistical parameters like minimum, maximum, mean, standard deviation of total torque, torque ripple coefficient and the settling time of speed response for various controllers are reported. From the simulation results, it is found that both FLC and ANFIS controllers gives better performance than PI controller. Among the intelligent controllers, ANFIS gives outer performance than FLC due to its good learning and generalization capabilities thereby improves the dynamic performance of SRM drives.