Robust predictive control for respiratory CO2 gas removal in closed-loop mechanical ventilation: An in-silico study

In this study a physiological closed-loop system for arterial CO2 partial pressure control was designed and comprehensively tested using a set of models of the respiratory CO2 gas exchange. The underlying preclinical data were collected from 12 pigs in presence of severe changes in hemodynamic and p...

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Autores principales:	Schmal Matthias, Haueisen Jens, Männel Georg, Rostalski Philipp, Kircher Michael, Bluth Thomas, Gama de Abreu Marcelo, Stender Birgit
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2020
Materias:	data-based modeling physiological closedloop control closed-loop mechanical ventilation co2 gas exchange Medicine R
Acceso en línea:	https://doaj.org/article/fa22995b533d41f887869e429fbadb9b
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spelling	oai:doaj.org-article:fa22995b533d41f887869e429fbadb9b2021-12-05T14:10:42ZRobust predictive control for respiratory CO2 gas removal in closed-loop mechanical ventilation: An in-silico study2364-550410.1515/cdbme-2020-3080https://doaj.org/article/fa22995b533d41f887869e429fbadb9b2020-09-01T00:00:00Zhttps://doi.org/10.1515/cdbme-2020-3080https://doaj.org/toc/2364-5504In this study a physiological closed-loop system for arterial CO2 partial pressure control was designed and comprehensively tested using a set of models of the respiratory CO2 gas exchange. The underlying preclinical data were collected from 12 pigs in presence of severe changes in hemodynamic and pulmonary condition. A minimally complex nonlinear state space model of CO2 gas exchange was identified post hoc in different lung conditions. The control variable was measured noninvasively using the endtidal CO2 partial pressure. For the simulation study the output signal of the controller was defined as the alveolar minute volume set value of an underlying adaptive lung protective ventilation mode. A linearisation of the two-compartment CO2 gas exchange model was used for the design of a model predictive controller (MPC). It was augmented by a tube based controller suppressing prediction errors due to model uncertainties. The controller was subject to comparative testing in interaction with each of the CO2 gas exchange models previously identified on the preclinical study data. The performance was evaluated for the system response towards the following five tests in comparison to a PID controller: recruitment maneuver, PEEP titration maneuver, stepwise change in the CO2 production, breath-hold maneuver and a step in the reference signal. A root mean square error of 2.69 mmHg between arterial CO2 partial pressure and the reference signal was achieved throughout the trial. The reference-variable response of the model predictive controller was superior regarding overshoot and settling time.Schmal MatthiasHaueisen JensMännel GeorgRostalski PhilippKircher MichaelBluth ThomasGama de Abreu MarceloStender BirgitDe Gruyterarticledata-based modelingphysiological closedloop controlclosed-loop mechanical ventilationco2 gas exchangeMedicineRENCurrent Directions in Biomedical Engineering, Vol 6, Iss 3, Pp 311-314 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	data-based modeling physiological closedloop control closed-loop mechanical ventilation co2 gas exchange Medicine R
spellingShingle	data-based modeling physiological closedloop control closed-loop mechanical ventilation co2 gas exchange Medicine R Schmal Matthias Haueisen Jens Männel Georg Rostalski Philipp Kircher Michael Bluth Thomas Gama de Abreu Marcelo Stender Birgit Robust predictive control for respiratory CO2 gas removal in closed-loop mechanical ventilation: An in-silico study
description	In this study a physiological closed-loop system for arterial CO2 partial pressure control was designed and comprehensively tested using a set of models of the respiratory CO2 gas exchange. The underlying preclinical data were collected from 12 pigs in presence of severe changes in hemodynamic and pulmonary condition. A minimally complex nonlinear state space model of CO2 gas exchange was identified post hoc in different lung conditions. The control variable was measured noninvasively using the endtidal CO2 partial pressure. For the simulation study the output signal of the controller was defined as the alveolar minute volume set value of an underlying adaptive lung protective ventilation mode. A linearisation of the two-compartment CO2 gas exchange model was used for the design of a model predictive controller (MPC). It was augmented by a tube based controller suppressing prediction errors due to model uncertainties. The controller was subject to comparative testing in interaction with each of the CO2 gas exchange models previously identified on the preclinical study data. The performance was evaluated for the system response towards the following five tests in comparison to a PID controller: recruitment maneuver, PEEP titration maneuver, stepwise change in the CO2 production, breath-hold maneuver and a step in the reference signal. A root mean square error of 2.69 mmHg between arterial CO2 partial pressure and the reference signal was achieved throughout the trial. The reference-variable response of the model predictive controller was superior regarding overshoot and settling time.
format	article
author	Schmal Matthias Haueisen Jens Männel Georg Rostalski Philipp Kircher Michael Bluth Thomas Gama de Abreu Marcelo Stender Birgit
author_facet	Schmal Matthias Haueisen Jens Männel Georg Rostalski Philipp Kircher Michael Bluth Thomas Gama de Abreu Marcelo Stender Birgit
author_sort	Schmal Matthias
title	Robust predictive control for respiratory CO2 gas removal in closed-loop mechanical ventilation: An in-silico study
title_short	Robust predictive control for respiratory CO2 gas removal in closed-loop mechanical ventilation: An in-silico study
title_full	Robust predictive control for respiratory CO2 gas removal in closed-loop mechanical ventilation: An in-silico study
title_fullStr	Robust predictive control for respiratory CO2 gas removal in closed-loop mechanical ventilation: An in-silico study
title_full_unstemmed	Robust predictive control for respiratory CO2 gas removal in closed-loop mechanical ventilation: An in-silico study
title_sort	robust predictive control for respiratory co2 gas removal in closed-loop mechanical ventilation: an in-silico study
publisher	De Gruyter
publishDate	2020
url	https://doaj.org/article/fa22995b533d41f887869e429fbadb9b
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Robust predictive control for respiratory CO2 gas removal in closed-loop mechanical ventilation: An in-silico study

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