Comparison of BLDC motor controller design for electric vehicles using fuzzy logic controller and artificial neural network

The existence of technologies such as electric motors such as the Brushless DC Motor become one of the solutions to replace the fossilfueled engine. In order for the BLDC motor to rotate at controlled speed, a closed system is needed which can correct the actual speed or error when the BLDC motor is spinning. In this final project the speed control method used is Pulse Width Modulation (PWM) and the actual speed feedback will be controlled using fuzzy logic controller so that the actual speed can be set according to the desired speed. In this paper, compared two control method FLC and artificial neural network (ANN). Therefore the simulation and implementation are made and the data obtained that the results of the implementation that have been made are approaching from the simulation results. The difference between the results of the implementation and the simulation is because when implementing the speed sensor used has an average error of 2.11% so that the resulting actual speed also varies. From the results of the implementation data, errors that occur up to 1.28%. Simulation result have an average error of 58.9% with ANN.