Speed Control of Brushless DC Electric Motor (BLDC) Motor Using Hybrid Takagi Sugeno Fuzzy Logic and Enhanced Gravitational Search Algorithm with PSO

Abstract

The brushless direct current motors have gained popularity among the emerging technologies due to their increased efficiency, speed of operation, and density of flux. This paper presents a novel technique for controlling the speed of the brushless direct current motor by using hybrid algorithms. The Particle Swarm Optimization (PSO) algorithm is combined with the Takagi Sugeno adaptive fuzzy interference system and gravitational search algorithms to analyze the speed control of the brushless DC electric motor (BLDC) motors. The proposed model has designed multiple outputs prototype for BLDC motor with Takagi Sugeno fuzzy logic. Since the adaptive fuzzy logic system has three outputs, the final output is evaluated as the average value of all the outputs using the LMS algorithm. The speed control of the Brushless DC motor has also been analyzed using a combination of the gravitational search algorithm and the PSO algorithm. The motor parameters taken under consideration for the analysis have been tabulated. The different results obtained at the end of this study have been demonstrated through graphs and tables.