Optimized Evaluation of Brushless Motor Drive System using Adaptive Neuro-Fuzzy, PSO & Inference of Genetic Algorithm
Abstract: A new proposed technology is introduced here, an Adaptive Neuro-Fuzzy Inference System (ANFIS) with a supervisory learning algorithm, is designed and developed here to standardize the speed and optimize the transient response of the DC (BLDC) motor drive system. The supervisory algorithm design- based ANFIS controller is applied in this proposed paper. Many industrial applications are required with high speed, high torque, high efficiency, and low volume, which will be found in the BLDC motor. This research aims to develop a complete model of the BLDC motor and design an optimal controller for its rotor position control. A PSO controller is generally designed and implemented for many control problems due to its simple structure and easy implementation. In optimizing the current distortion and torque reduction in the BLDC motor, a good genetic algorithm is proposed as a universal optimizer to find the optimized PSO particles. Therefore, this proposed concept is designed and developed in three stages to minimize the distortion where GA has implemented optimized TPBLDCM. It is also considered to evaluate and improve the effectiveness and performance issues of the BLDC motor with this proposed algorithm. Finally, this concept will be implemented in Simulink/MATLAB with a comparative analysis of the BLDC motor and its modeling. The results encourage better performance than the conventional controllers.
Index: BLDC motor, ANFIS, GA, Crossover of PSOA, Objective-function, Mutation, Performance Analysis.