Neural Network-Enhanced Nonlinear PID Control: Advances in Stability, Robustness, and Real-World Application

With the continuous advancement in the field of automatic control, enhancing the robustness of control systems to ensure their reliable operation under various uncertain interference factors has become increasingly crucial. Compared to traditional linear control systems, nonlinear control systems demonstrate superior robustness when confronted with complex disturbances and can effectively prevent issues such as system overshoot. Neural networks, as computational models composed of multiple interconnected neurons, exhibit strong nonlinear modeling capabilities. Consequently, neural networks can be utilized to enhance nonlinear PID controllers, further improving their adaptability. Besides, neural networks have self-learning capabilities, and that makes them very suitable for dynamic and uncertain environments. This work reviews the recent progress of research on the stability of PID controllers and investigates several control methods for nonlinear systems with their advantages and disadvantages, considering the neural network improvements. Then, the application of those controllers in real production scenarios is given in detail. At last, this paper addresses some current limitations of research in this area and gives some insights into the future development based on how to advance the research and application of neural network-enhanced nonlinear PID control algorithms.