A Study on the Optimization of PID Control Parameters Using Machine Learning

Abstract

The PID control method is widely used in automatic control systems, especially for motor control, to achieve efficient control through proportional, integral, and derivative gain values. Since performance heavily depends on proper tuning, traditional methods like trial and error or frequency response checks are commonly used but have drawbacks, including time consumption and neglect of system-specific characteristics. This paper proposes a machine learning-based optimization technique to address these limitations and enhance PID performance. By leveraging machine learning algorithms to determine optimal gain values, this approach enables adaptive tuning tailored to system characteristics, offering faster and more precise results compared to traditional methods. This study is conducted through the following research process. First, a simulation environment is constructed for the motor control system to which PID control is applied. Second, the operation data is collected from various initial conditions and environmental variables. Third, the machine learning algorithm is trained based on the collected data to derive the optimal gain value suitable for the system response. Finally, the effectiveness is verified by comparing and analyzing the performance of the machine learning-based optimization technique with the existing method. In particular, this paper focuses on how data-driven approaches differentiate themselves from existing empirical methods. Machine learning has great potential in maximizing the efficiency of PID controllers as it can flexibly cope with system nonlinear characteristics and environmental changes. The results of this study are expected to contribute to the performance improvement of PID control systems, as well as to demonstrate the practicality of data-driven optimization approaches in the field of automatic control.