Speed Control of DC Motor Using Artificial Neural Network

This paper uses Artificial Neural Networks (ANNs) in estimating speed and controlling it for a separately excited DC motor which is one of the most important modern techniques that using in control applications and to improve efficiency speed control of separately excited DC motor (SEDM). The rotor speed of the DC motor can be made to follow an arbitrarily selected trajectory. The purpose is to achieve accurate trajectory control of the speed, especially when the motor and load parameters are unknown. Such a neural control scheme consists of two parts. One is the neural identifier which is used to estimate the motor speed. The other is the neural controller which is used to generate a control signal for a converter. These two neural networks are trained by Levenberg- Marquardt back-propagation algorithm. In this paper, the intelligent model is developed to speed control of SEDM which operated at two stages-the first, NARMA-L2 controller used to control the speed under different external loads conditions. The second, the controller is performance at different reference speed. Simulation results indicates to the advantages, effectiveness, good performance of the artificial neural network controller which is illustrated through the comparison obtain by the system when using conventional controller (Proportional-Integral (PI)). So the results show ANN techniques provide accurate control and ideal performance at real time