International Research Journal of Education and Technology

Artificial Neural Network (ANN) is the simple network that has input, output, and hidden layers with a set of nodes. Implementation of ANN algorithms in electrical, and electronics engineering always satisfies with the expected results   as         ANN                           handles                 binary    data   more accurately.  Brushless                      Direct              Current                         motor (BLDC     motor)           uses    electronic                   closed-loop controllers to the switch DC current to the motor windings and produces the magnetic fields. The BLDC motor finds more applications because of its high speed, less maintenance and adequate torque capability. This motor is preferred to other motors due to its better performance and it is very easy to control its speed by Power Converters. This article presents a method of speed control of BLDC motor where      speed is controlled by changing the DC input voltage of the bridge converter that feeds the motor winding. The control is done by using a PI based speed controller.     The    motor    is     modeled    in    the MATLAB/Simulink and the speed control is obtained with a PI controller. EMF signals, rotor speed, electromagnetic torque, Hall Effect signals, PWM and EMF signals simulations are obtained. The obtained data is fed into binary artificial neural networks and as a result, the ANN model predicts the corresponding parameters close to the simulation results. Both the mathematical based simulation and data-based prediction gives satisfactory results.