Improving Pressure Control in Artificial Ventilation Systems Using a Neural Network-Based Adaptive PID Controller

Artificial ventilation systems play a crucial role in the field of respiratory care, especially in intensive care units and surgical environments, where patients often require assisted breathing due to conditions such as acute respiratory distress syndrome (ARDS) or the effects of anesthesia. This study focuses on the development of an adaptive proportional–integral–derivative (PID) controller enhanced by a backpropagation neural network (BPNN) algorithm to accurately track airway pressure throughout the mechanical ventilation process. To achieve this, a MATLAB/Simulink model of a blower-driven patient hose (BDPH) ventilator system is constructed. Then, the performance efficiency of the artificial ventilation system is evaluated and analyzed based on the proposed control scheme under different operational scenarios. Furthermore, an analysis and comparison study of the performance of the adaptive PID controller based on the BPNN method and the classical PID controller has been conducted in terms of the robustness properties and transient behavior of the system. Simulation outcomes indicate that the adaptive PID controller showed faster convergence to the target airway pressure compared to the classical PID controller. This performance advantage arises from the controller’s ability to continuously adapt its gains to changes in operational conditions.