Modeling Simulation of BLDC Motor and HEV Motor System Using Adaptive Neuro Fuzzy (ANFIS) Inference Algorithm

Abstract

The main concept of new adaptive neuro fuzzy inference system (ANFIS) along by means of supervisory learning algorithm isutilized for the regulation of speed that that is reduced by rise time and setting time of brushless DC (BLDC) that is hybrid motor motor system. Here the main advantage of the present algorithm is neural and fuzzy networks so in order to enhance the performance of electrical motor motor a well known adaptive neuro fussy ANN (ANFIS) technique that is based on supervisory learning algorithm is introduced. This method is deliberated in such a way to control the torque, startup power and also for improving the dynamic performance of the system. Here this technique follows minimum fuzzy rules and membership functions for implementation of system that is relatively easy and also it is compared by conventional fuzzy neural networks that are basically utilized for electrical motor applications. Here for demonstration and controlling of the proposed ANFIS they follow the speed reference by rejecting disturbance it perform simulation and compares that with conventional ANN manager.