Acute respiratory failure is one of the major causes for concern in intensive care units (ICU). It is a condition that occurs when fluid builds up in the air sacs in the lungs. When this happens the lungs cannot release oxygen into the blood and get rid of carbon dioxide from the body. The lack of oxygen in the blood will cause the organs to malfunction and in severe cases could lead to failure of vital organs such as heart and brain. Patients with acute respiratory failure are usually supported by the mechanical ventilator. The goal of mechanical ventilation is to ensure adequate ventilation, which involves a magnitude of gas exchange that leads to the desired blood level of carbon dioxide, and adequate oxygenation, which involves a blood concentration of oxygen that will ensure proper function of vital organs. A plausible reference lung volume pattern, which corresponds to an optimal patient outcome, is specified by clinical personnel and the task of automated mechanical ventilation is to apply appropriate input pressure so that the output from the system is able to track the given reference voulme pattern. One of the main challenges in mechanical ventilation that needs to be carefully considered is that the applied pressure cannot be arbitrarily large since large applied pressure could potentially damage the lungs, and hence, worsen patients outcome. Another challenge in designing an efficient control algorithm for mechanical ventilation is that the parameters characterizing the dynamics of the lungs, that is, the lung resistances and lung compliances, vary from patient to patient as well as within the same patient under different conditions. Furthermore, the volume of each compartment of the lungs cannot be directly measured and only the total volume of the lungs is available. Therefore, a robust control methodology, which only uses output information and limited applied pressure, is needed in order to design an efficient control algorithm for automated mechanical ventilation. In this research work, we propose an output-feedback sliding mode control methodology for a nonlinear multicompartment respiratory system with amplitude and integral input constraints. The amplitude input constraint is needed to ensure that the applied pressure is not too large, as not to damage the lungs, while the integral input constraint enforces the upper bound on the amount of work done by the ventilator. The proposed controller only uses output information (i.e., the total volume of the lungs) and automatically adjusts the applied input pressure so that the system is able to track a given reference volume pattern in the presence of parameter uncertainty (i.e., modeling uncertainty of the lung resistances and lung compliances) and system disturbances. A Lyapunov-based approach is presented for the stability analysis of the system and the proposed control framework is applied to a two compartment lung model to show the efficacy of the proposed control method.


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