Adaptive Control for Mechanical Ventilation for Improved Pressure Support

Respiratory modules are medical devices used to assist patients to breathe. The aim of this article is to develop a control method that achieves exact tracking of a time-varying target pressure, for unknown patient-hose-leak parameters and in the presence of patient breathing effort. This is achieved by an online estimation of the hose characteristics that enables compensation for the pressure drop over the hose. Stability of the closed-loop system is proven, and the performance improvement compared to the existing control strategies is demonstrated by simulation and experimental case studies.

[1]  Olaf Simanski,et al.  Model-based control approach for a CPAP-device considering patient’s breathing effort , 2017 .

[2]  Hancao Li,et al.  Model predictive control for a multi-compartment respiratory system , 2012, 2012 American Control Conference (ACC).

[3]  Carin A. Hagberg,et al.  Benumof and Hagberg's Airway Management. , 2012, European journal of anaesthesiology.

[4]  Bram Hunnekens,et al.  Variable-Gain Control for Respiratory Systems , 2020, IEEE Transactions on Control Systems Technology.

[5]  Arthur S Slutsky,et al.  Driving pressure and survival in the acute respiratory distress syndrome. , 2015, The New England journal of medicine.

[6]  C M DRUMMOND,et al.  MECHANICAL VENTILATION. , 1964, North Carolina medical journal.

[7]  Robert M. Kacmarek,et al.  Asynchronies during mechanical ventilation are associated with mortality , 2015, Intensive Care Medicine.

[8]  Anuradha M. Annaswamy,et al.  Robust Adaptive Control , 1984, 1984 American Control Conference.

[9]  M. A. Borrello Adaptive inverse model control of pressure based ventilation , 2001, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148).

[10]  Olaf Simanski,et al.  Iterative Learning Control: An Example for Mechanical Ventilated Patients , 2015 .

[11]  C. Carvalho,et al.  Effect of a protective-ventilation strategy on mortality in the acute respiratory distress syndrome. , 1998, The New England journal of medicine.

[12]  Hancao Li,et al.  Model Predictive Control for a Multicompartment Respiratory System , 2013, IEEE Transactions on Control Systems Technology.

[13]  Peter J Pronovost,et al.  Projected incidence of mechanical ventilation in Ontario to 2026: Preparing for the aging baby boomers* , 2005, Critical care medicine.

[14]  B. Lachmann,et al.  Open up the lung and keep the lung open , 1992, Intensive Care Medicine.

[15]  P. Navalesi,et al.  Bench studies evaluating devices for non-invasive ventilation: critical analysis and future perspectives , 2011, Intensive Care Medicine.

[16]  Jean-François Muir,et al.  Realistic human muscle pressure for driving a mechanical lung , 2014 .

[17]  M. Borrello Modeling and control of systems for critical care ventilation , 2005, Proceedings of the 2005, American Control Conference, 2005..

[18]  Berno J. E. Misgeld,et al.  Periodic funnel-based control for peak inspiratory pressure , 2015, 2015 54th IEEE Conference on Decision and Control (CDC).

[19]  Nathan van de Wouw,et al.  Switching control of medical ventilation systems , 2018, 2018 Annual American Control Conference (ACC).