Online control for pressure regulation of oxygen mask based on neural network

The aviation oxygen mask, which has a small volume of less than 1 L and strong air tightness, imposes extremely high requirements on control performance of the oxygen regulator. Based on analyses of the operation principle of oxygen supply system, the dynamic model is established through the combination of mechanism analysis and experimental data. Considering that the traditional fixed-parameter controllers are difficult to meet the control requirements with changes in pulmonary ventilation, this paper presents an online feedback controller based on neural network compensation (NNC), with connection weights that can be updated without pre-training. Then mathematical simulations at different respiratory parameters, such as respiratory rate, are performed to verify the superior lower inspiratory resistance of controller with NNC. In terms of hardware, an embedded AI control platform is to complete the experimental verification. Furthermore, the work may have downward compatibility to achieve stable oxygen supply in civil fields, such as medical ventilators, high-altitude expeditions.