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The continuous positive airway pressure (CPAP) therapy is the gold standard to treat the obstructive sleep apnoea syndrome. This continuous pressure splints the upper respiratory tracts and prevents a collapse of the pharynx. If not being treated a collapse of the upper respiratory tracts reduces the blood oxygen concentration and increases the CO2 concentration leading to repeated sleep disruptions. Many CPAP-devices cannot maintain the reference pressure, which can lead to an uncomfortable perception for the patient. To reduce the pressure deviation in the mask we want to apply the Iterative Learning Control (ILC) method, because the breathing of the patient is a repetitive process - and at night the breathing patterns are even and recurring from breath to breath.

The obstructive sleep apnoea syndrome (OSAS) is characterized by a collapse of the upper respiratory tract, resulting in a reduction of the blood oxygen- and an increase of the carbon dioxide (CO2) - concentration, which causes repeated sleep disruptions. The gold standard to treat the OSAS is the continuous positive airway pressure (CPAP) therapy. The continuous pressure keeps the upper airway open and prevents the collapse of the upper respiratory tract and the pharynx. Most of the available CPAP-devices cannot maintain the pressure reference [1]. In this work a model predictive control approach is provided. This control approach has the possibility to include the patient’s breathing effort into the calculation of the control variable. Therefore a patient-individualized control strategy can be developed.