Performance Analysis of Various Optimization Methods for PID Controller in DC Motor Control

Proportional-integral-derivative (PID) controllers are widely used in industrial control systems for their simplicity, robustness, and reliability. This paper evaluates the performance of various optimization techniques for tuning PID controllers, addressing the trade-off between computational cost and the improved accuracy and robustness achieved through these methods. The study investigates tuning techniques such as Genetic Algorithm (GA), Golden Eagle Optimizer (GEO), Process Reaction Curve, Ziegler-Nichols, Auto-tuning, and Trial-and-Error methods. A detailed comparison is provided, with Trial-and-Error serving as a baseline, to highlight the advantages, limitations, and applicability of each method for different control systems. The results emphasize the effectiveness of the Genetic Algorithm in meeting specific control system requirements, demonstrating its potential to achieve optimal performance.