Iterative Learning Control: An Example for Mechanical Ventilated Patients. Issue 20 (2015)

• Record Type: Journal Article
• Authors: Scheel, M.; Berndt, A.; Simanski, O.
• Is Part Of: IFAC-PapersOnLine. Volume 48:Issue 20(2015)
• Page Start: 523
• Page End: 527
• Publication Date: 2015
• Subjects: mechanical ventilation -- pressure control ventilation -- control design -- iterative learning control; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2015.10.194
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Positive pressure ventilation is a method of artificial ventilation. If patients can not breath normally due to respiratory insufficiency, they are connected to a mechanical ventilator. The ventilator generates a positive end-expiration pressure (PEEP) during the expiration phase and an inspiratory positive airway pressure (IPAP) during the inspiration phase. These different pressure levels lead to inflation and deflation of the lung and the patient is ventilated. To achieve the desired pressure levels a closed-loop pressure control has to be designed and developed. Linear PI(D)-controller for example can not follow predefined reference trajectories exactly, because of different and varying patient states and lung parameters. An adaption from patient to patient or from breathing cycle to breathing cycle is usually not possible. Due to further development in microcontroller technology, more complex control algorithms can be used. For cyclically recurring processes iterative learning control (ILC) algorithms provide the possibility to react on variable environment conditions. In ventilation the ILC algorithm can learn the required signal from breathing cycle to breathing cycle to track the reference trajectory.