Certain investigation on torque ripple minimization in brushless dc motor using intelligent control techniques

Abstract

The Brushless Direct Current (BLDC) motor is widely used in many applications such as, domestic equipment, automobiles, information technology equipment, transportation, military, aerospace, defence equipment, power tools, toys, vision and sound equipment etc. The features of BLDC motors are high quality, high capability, fast dynamic response, low maintenance, high power to weight ratio, high speed and good electric control etc. One of the important parameters in BLDC motor is its torque. Due to load fluctuation, transients and dynamic change in electrical parameters, this torque may have ripples. So, torque ripple minimization of BLDC motor is considered in this research work. This research work proposes, an improved method called Direct Torque Control (DTC) technique, which improves the speed and torque characteristics of the BLDC motor. Gravitational Search Algorithm (GSA) and the Radial Basis Function Neural Network (RBFNN) method are implemented in this improved method. By redesigning the randomized structure, the new performance of the GSA is obtained. The RBFNN is developed for the execution of the GSA. The FOPID is implemented for speed and torque controlof BLDC motor. This proposed method is simulated in the MATLAB platform. The suggested technique is recognized and combined with the modern strategies such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO)-RBFNN procedures newline