Motor Speed Control Using PID Control with a Metaphysical Approach: A Comprehensive Analysis

Abstract

Recent advancements in control technology have become integral to various industrial applications worldwide. Among these advancements, direct current (DC) motors are commonly utilized actuators in industrial systems due to their simplicity and reliability. DC motors exhibit a fast dynamic response but tend to experience steady-state errors, which can affect system performance. To address this challenge, it is crucial to implement an appropriate controller that optimally aligns with the inherent characteristics of DC motors. A Proportional-Integral-Derivative (PID) controller is widely recognized for its ability to provide fast response and effective speed control in DC motor systems. This study explores the application of a novel approach that integrates metaheuristic techniques, specifically Genetic Algorithms (GAs), to optimize the parameters of the PID controller. Unlike traditional PID tuning methods, such as trial and error, Ziegler-Nichols, or manual optimization, the metaheuristic optimization technique offers distinct advantages in achieving faster settling times and minimizing steady-state errors, while also reducing overshoot. The primary goal of this research is to enhance PID control efficiency for DC motors, particularly focusing on obtaining optimal gain parameters for improved performance in various industrial applications. The metaheuristic optimization approach applied in this study involves the use of Genetic Algorithms to fine-tune the PID gains, leading to superior control system performance. The results of this optimization are compared with conventional PID tuning methods through simulation using MATLAB/Simulink. The comparative analysis highlights the effectiveness of the proposed method in terms of quicker stabilization and improved steady-state accuracy.