Design and Simulation of an Intelligent PID Controller Using Neural Network Tuning and Industrial Disturbance Modeling in MATLAB

Abstract

This paper presents the design and simulation of an intelligent PID controller integrated with neural network-based tuning and industrial disturbance modeling using MATLAB. A graphical user interface (GUI) was developed to allow interactive adjustment of PID parameters (Kp, Ki, Kd) and automatic tuning via a trained artificial neural network (ANN). The system targets a first-order industrial process model, such as a liquid tank, and introduces simulated disturbances to evaluate controller robustness. The ANN was trained on performance data from various PID configurations to predict optimal gains that minimize steady-state error and improve dynamic response. The simulation includes both reference tracking and disturbance rejection scenarios, offering a realistic industrial control environment. Results demonstrate that the AI-enhanced PID controller outperforms manual tuning in terms of settling time, overshoot reduction, and adaptability to external changes. This hybrid approach bridges classical control theory with modern AI techniques, providing a flexible and intelligent solution for industrial automation and educational applications.