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
