Speed Control Of Brushless Dc Motor Using Fuzzy Based Model Reference Adaptive Control

Abstract

Brushless DC motor drives are widely used in various industrial systems, such as servomotor drives, medical, automobile and aerospace industry. BLDC motors are commutated electronically and offer many advantages over brushed DC motors which include increased efficiency, longer life, low volume and high torque. Speed control of BLDC motor with drastically changing and uncertain load variation with conventional PID control system is unable to cup up with and also unable to adapt system load variation with constant gain values. So, the main objective of this thesis is to design a controller to keep the output speed of the BLDC motor constant, under different operating conditions such as parameter variations at rated speed, uncertain load disturbances and etc. In Fuzzy intelligent controller Based model reference adaptive control, the plant output is varied with respect to the output of the reference model with some adjusting mechanism in order to obtain the speed control at operating condition. The platform for modelling of BLDC motor and simulation of the control system has been done using MATLAB/Simulink Software and the result shows that intelligent based model reference adaptive controller is better in steady state performance and load rejection property than simple MIT rule and conventional PID controller and improved the existing system of the steady state response