Separately excited DC motor speed using ANN neural network
Zuhair Shakor Mahmood, Ali Najdet Nasret, Omar Talal Mahmood
Abstract
Separately Excited Direct Current Motors (SEDCM) is termed by high efficiency in electrical traction manufacturing. SEDCMs are utilized by high power applications such as aircrafts and ships traction. Speed control is more likely required to maintain the performance of the motor in different load scenarios. Conventional methods of speed control such as proportional integral controller (PIC) are reported good performance in error tackling but it may consume longer time and computational cost. In this paper, computational speed controller is proposed which uses neural network to predict speed and then to produce the reference voltage in order to update the armature terminal voltage. In this study have been assumed a three layers neural network to implement the control on speed of motor and the model is outperformed over the conventional controller models.