Speed Control of BLDC Motor using Soft Computing Technique

Brushless DC Motor (BLDCM) is widely used than other motors to achieve high efficiency, high pf, accurate speed and torque control. Brushless DC motor has gained more attention in the areas like electric trains, aviation and robotics. BLDC motors have the capacity to exchange over other conventional motors, however the main drawback is its drive system implementation which should be effective, efficient and enforced at ease. So, an attempt is made to implement a better speed control system for a Brushless DC motor using Artificial Neural Network (ANN) and available conventional controllers is carried out. Here the planning, performance and the comparative study between the speed control of Brushless DC Motor using conventional controller like Proportional Integral controller (PI) and ANN enhanced Proportional Integral controller is presented. A simulation study is conducted to check the efficiency of the proposed system. A relative study is performed to authenticate the effectiveness of the system. We have used Matlab/Simulink for the simulations of circuits. Here, nonlinear mapping capability of NNs and tuning method of PI controller based on Back-Propagation (BP) method is utilized.