The Limitations of Traditional PID Controllers and Modern Optimization Methods

The Proportional-Integral-Derivative (PID) controller, due to its simple structure and strong robustness, is widely used in industrial control systems. However, as industrial control environments grow more complex, traditional PID controllers encounter challenges like fixed parameters and difficulty adapting to nonlinear systems. In response, various improvement strategies have been proposed in recent years, such as optimization algorithms, adaptive control, and machine learning methods, all aimed at enhancing the performance and adaptability of PID control. The study investigates the limitations of traditional PID controllers and analyzes recent advancements in modern improvement techniques through a literature review. By reviewing the basic components of PID controllers, parameter tuning methods, and their enhancement strategies, this research highlights how these approaches effectively improve the response time, stability, and robustness of PID controllers. The results indicate that, with the application of improvement strategies like optimization algorithms and adaptive control, PID controllers can better cope with dynamically changing environments, enhancing control accuracy and system stability.