Direct Current Motor Speed Control Using Genetic Algorithm Based Proportional Integral Derivative (PID) Method

The control of Direct Current (DC) motor speed is a critical function in numerous industrial and automation processes. Traditional Proportional-Integral-Derivative (PID) controllers have been widely used, but they often require manual tuning, which may not yield optimal results in dynamic environments. This thesis investigates the use of Genetic Algorithms (GAs) to optimize PID parameters for improved speed control of DC motors. A mathematical model of a separately excited DC motor was developed, and both classical PID and GA-optimized PID controllers were implemented in MATLAB/Simulink. Comparative analysis was conducted based on system performance metrics such as rise time, settling time, peak overshoot, and steady-state error. The results demonstrate that the GA-optimized PID controller significantly enhances performance, providing faster response, reduced overshoot, and higher stability under variable load conditions.