Design and Simulation of an Intelligent PID Controller Using Neural Network Tuning and Industrial Disturbance Modeling in MATLAB - Salim Abdallah Alhussain; Mahfoud Y. A. Abdulrazigh (African Journal of Advanced Pure and Applied Sciences, 2026)
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.