Application of Neural Networks for the Control and Speed Regulation of Separately Excited DC Motor

Our study focuses on the application of neural networks for the control and regulation of the speed of a separately excited DC machine. To do this, we conducted a comparative study of our intelligent control with a traditional control method such as PID. The objective of this comparison is to better evaluate the performance criteria of our intelligent system in terms of speed, accuracy, and robustness against load variations. The work includes modeling of the separately excited DC machine, tuning of PID parameters, learning and implementation of the neural block on our system, and simulation of both control methods using the MATLAB/Simulink environment. The simulation results have demonstrated that the neural control exhibits significantly better speed regulation performance compared to the traditional PID control.