Robust model predictive control of an anaesthesia workstation ventilation unit

Georg Männel; Marlin Siebert; Christian Brendle; Philipp Rostalski

doi:10.1515/auto-2020-0087

Robust model predictive control of an anaesthesia workstation ventilation unit

Titel in Übersetzung: Robuste Modellprädiktive Regelung der Beatmungseinheit eines Anästhesiearbeitsplatzes

Georg Männel^*, Marlin Siebert, Christian Brendle, Philipp Rostalski

^*Korrespondierende/r Autor/-in für diese Arbeit

Institut für Medizinische Elektrotechnik

Dräger Medical

Abstract

Atemtherapie ist ein wesentliches Element moderner Medizin. Die Bandbreite reicht dabei von der Sauerstoffzugabe bis zur vollständigen maschinellen Beatmung. Eine zentrale Komponente der Beatmung ist die Regelung der resultierenden pneumatischen Größen wie Druck und Volumenstrom. Mit dem Ziel, die Patientensicherheit weiter zu verbessern, wird in diesem Artikel die Verwendung von robusten, modellprädiktiven Regelansätzen zur Druckregelung in der druckkontrollierten Beatmung diskutiert. In dieser Veröffentlichung wird ein Ansatz verfolgt, bei dem die Modellunsicherheiten des physiologischen Modells in einem robusten modellprädiktiven Regelungskonzept explizit berücksichtigt werden. Die praktische Umsetzung dieses Ansatzes wird anhand eines Forschungsdemonstrators der Ventilationseinheit eines Anästhesiegerätes präsentiert.

Titel in Übersetzung	Robuste Modellprädiktive Regelung der Beatmungseinheit eines Anästhesiearbeitsplatzes
Originalsprache	Englisch
Zeitschrift	At-Automatisierungstechnik
Jahrgang	68
Ausgabenummer	11
Seiten (von - bis)	941-952
Seitenumfang	12
ISSN	0178-2312
DOIs	https://doi.org/10.1515/auto-2020-0087
Publikationsstatus	Veröffentlicht - 26.11.2020

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title = "Robust model predictive control of an anaesthesia workstation ventilation unit",

abstract = "Respiratory support is a key element of modern medical care, ranging from oxygen therapy to full ventilatory support. A central component of mechanical ventilation is the control of the resulting pneumatic quantities such as pressure and flow. In this article the use of robust model predictive control for pressure-controlled mechanical ventilation is proposed, with the goal of increasing the safety of the patient by considering physiological safety constraints. The uncertainty in the estimation of physiological model parameters as well as model uncertainties are considered as disturbances to the system, which are taken into account through the proposed robust model predictive control framework. The practical applicability of this control approach is illustrated in an implementation on a research demonstrator of the ventilation unit from an anaesthesia workstation. ",

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N2 - Respiratory support is a key element of modern medical care, ranging from oxygen therapy to full ventilatory support. A central component of mechanical ventilation is the control of the resulting pneumatic quantities such as pressure and flow. In this article the use of robust model predictive control for pressure-controlled mechanical ventilation is proposed, with the goal of increasing the safety of the patient by considering physiological safety constraints. The uncertainty in the estimation of physiological model parameters as well as model uncertainties are considered as disturbances to the system, which are taken into account through the proposed robust model predictive control framework. The practical applicability of this control approach is illustrated in an implementation on a research demonstrator of the ventilation unit from an anaesthesia workstation.

AB - Respiratory support is a key element of modern medical care, ranging from oxygen therapy to full ventilatory support. A central component of mechanical ventilation is the control of the resulting pneumatic quantities such as pressure and flow. In this article the use of robust model predictive control for pressure-controlled mechanical ventilation is proposed, with the goal of increasing the safety of the patient by considering physiological safety constraints. The uncertainty in the estimation of physiological model parameters as well as model uncertainties are considered as disturbances to the system, which are taken into account through the proposed robust model predictive control framework. The practical applicability of this control approach is illustrated in an implementation on a research demonstrator of the ventilation unit from an anaesthesia workstation.

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Robust model predictive control of an anaesthesia workstation ventilation unit

Abstract

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