AI-Augmented Pressure Fractional-Order Control in Hyperbaric Surgical Environments

Hyperbaric Oxygen Therapy surgical rooms require precise pressure control to ensure patient safety and therapeutic efficacy. This paper presents a comprehensive study on advanced control strategies for pressure regulation in hyperbaric environments. A dynamic process model of the hyperbaric chamber is developed, based on thermodynamic and fluid dynamics principles, and implemented in MATLAB/Simulink to simulate real-world operational scenarios. Three distinct control strategies are proposed and evaluated: a conventional Proportional-Integral-Derivative controller, a Fractional-Order PID controller for enhanced nonlinear system handling, and an Adaptive FOPID controller integrated with artificial neural networks to dynamically optimize control parameters in real time. Simulation results demonstrate that the Adaptive FOPID controller outperforms its counterparts, achieving superior transient response and reduced settling time. The integration of AI-driven neural networks enables adaptive tuning of fractional-order parameters, ensuring optimal performance across diverse pressure profiles. This work highlights the potential of adaptive fractional-order control systems in critical healthcare environments, offering a framework for safe, efficient, and reliable hyperbaric oxygen therapy.