Abstract
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.
Highlights
The inherent simplicity, ruggedness and low cost of a switched reluctance motor (SRM) make it a viable machine for various general purposes adjustable to speed drive applications
The primary disadvantage of SRM is the higher torque ripples when compared to conventional machines, which results in acoustic noise and vibration (HANY et al, 2010)
Results reveal that mean torque is increased and the torque ripple coefficient and standard deviation of total torque ( T) are reduced for Fuzzy Logic Controller (FLC) and adaptive neuro fuzzy inference system (ANFIS) controller
Summary
The inherent simplicity, ruggedness and low cost of a switched reluctance motor (SRM) make it a viable machine for various general purposes adjustable to speed drive applications. The fuzzy logic controller is not sufficient because torque ripples change with the SRM speed and load. The application of a fuzzy logic controller and adaptive neuro fuzzy inference system based on adding a compensating current signal to the switched reluctance motor to minimize the torque ripples is investigated.
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