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.
Zusammenfassung
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.
Funding statement: This work was partially funded by the Drägerwerk AG & Co. KGaA, Lübeck, Germany.
About the authors
Georg Männel, received the Master degree in medical engineering science from the University of Lübeck, Lübeck, Germany, in 2016. He is currently pursuing the Ph. D. degree with the Institute for electrical engineering in medicine, University of Lübeck, Lübeck, Germany. He conducted his Master thesis with the Drägerwerk AG & Co. KGaA from 2015 to 2016. His current research interest include modular hierarchical control, cyber physiological systems and safe learning-based control in medical applications.
Marlin Siebert holds a M. Sc. degree in Medical Engineering Science from Universität zu Lübeck, since 2019. His master thesis was on the optimization and robustification of the model predictive control to the ventilation system of an anaesthesia workstation in 2019. He was an intern at Drägerwerk AG & Co. KGaA in the department for research and development and worked on medical deep learning in 2018. Since 2020 he is employed as research associate at the Institute for Electrical Engineering in Medicine at the Universität zu Lübeck.
Christian Brendle, received the Ph. D. degree in electrical engineering from the RWTH Aachen University Germany, in 2018. He completed his dissertation on Cooperative Automation of the Therapy of the Acute Lung Failure with extracorporeal support and artificial ventilation at the Chair of Medical Information Technology of the RWTH Aachen University. Currently he develops novel medical systems at Drägerwerk AG & Co. KGaA, Lübeck, Germany. His current research interest include closed-loop controlled systems, modular medical devices and model based systems engineering in medical applications.
Prof. Dr. Philipp Rostalski is a Head of the Institute for Electrical Engineering in Medicine at the University of Lübeck. His research focuses on model- and data-driven methods in signal processing, estimation and control for safety critical systems. The primary application domains are biomedical and autonomous systems.
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