Speed Control of Brushless DC Motor Using Modified Genetic Algorithm Tuned Fuzzy Controller

Abstract

In the last decade with increasing motor application domain, need towards usage of precisely controlled, noise free, highly efficient and high starting torque motors also increases, as a result dedicated applications has fascinated the researcher toward brushless DC motor. Brushless DC motors can act as suitable alternative to the traditional Brushed direct current motor, Induction Motor etc. This research paper inspects the ease and effectiveness of modified queen bee based GA tuned fuzzy controller and shows the performance of a proposed controller under diverse speed settings. A comparative study with conventional PI controller shows effectiveness of modified queen bee based GA Tuned Fuzzy controller, in terms of parameter like peak overshoot and settling time. MATLAB/SIMULINK Environment is used for optimization and modeling of Brushless DC motor drive.

Optimal Adjustment of Evolutionary Algorithm-Based Fuzzy Controller for Driving Electric Motor with Computer Interface

“…Baoye S. et al, developed the approach using a combination of a random swam optimization (PSO) and gravitational search algorithm (GSA) for to design the optimal fuzzy proportional integrated (PI) controller for brushless DC motor (BLDCM) [14]. As studies in which genetic algorithms and fuzzy logic methods are used together in the literature; integrated fuzzy ga based unidirectional floating mode control [15], gains the setting of a fuzzy controller with genetic algorithms [16], closed loop speed control of the bldc motor driver using conventional controllers [17], the brushless dc motor's speed with genetic algorithm [18] and using a modified genetic algorithm tuned fuzzy controller [19].…”