PI Controller Based Switching Reluctance Motor Drives using Smart Bacterial Foraging Algorithm

Abstract

Optimization algorithms are commonly used in the industry. The optimization strategy, if key elements are ignored, can quickly render the solution unfeasible. As a result, various optimization strategies are applied at all aspects of the industry level. The switched reluctance motor is the most affordable of all motor types. The high torque density attribute of induction motors is one of the market's major drivers. Switched reluctance motors are also employed in high-volume and high-starting torque appliances. The Smart Bacterial Foraging Algorithm (SBFA) mimics the chemotactic behavior of E. Coli bacteria for optimization purposes. This method is used to calculate the coefficient of a typical Proportion–Integration (PI) speed controller for SRM drives while accounting for torque ripple reduction. The results of the modeling and experiments reveal that the modified PI controller with SBFA performs better. The proposed optimization strategy results in increased performance when compared to regular BFA.

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