Adaptive Fractional Sliding Mode Controller for Controlling Airway Pressure in an Artificial Ventilation System

Nowadays, artificial ventilation is one of the vital tools for medical purposes. The necessity to create an appropriate controller grows as these gadgets' dependability does. An essential piece of equipment for treating individuals with respiratory issues is a mechanical ventilator. Recently, pulmonary system failure has been observed in a significant proportion of Covid-19 patients. To guarantee a plentiful supply of mechanical ventilators amid the COVID-19 epidemic, the majority of global makers have embraced open-source designs. This work proposes a novel adaptive fractional order non-singular fast terminal sliding mode controller based on an ant colony optimization method for artificial ventilation system air pressure control. An Adaptive Sliding Mode Controller (ASMC) has been built for comparative purposes. Both ASMC and the suggested controller's parameters are modified via Ant Colony Optimization (ACO). Among the reliable control techniques is the sliding mode controller. This method is robust against external disturbances of the system. Optimizing the parameters increases the speed and accuracy of the proposed method. The effectiveness of the recommended approach is contrasted with Ant Colony Optimization, which is based on ASMC. In the end, the effectiveness of the recommended approach is evaluated in relation to the ASMC and against increased transmission rates (model uncertainty).