Robust tube-based predictive control of blood glucose concentration in type 1 diabetes

Harald Kirchsteiger; Luigi Del Re

doi:10.1109/CDC.2013.6760189

Robust tube-based predictive control of blood glucose concentration in type 1 diabetes

Harald Kirchsteiger
, Luigi Del Re

Institut für Maschinelle Intelligenz

Publikation: Beitrag in Buch/Bericht/Konferenzband › Konferenzbeitrag › Begutachtung

Abstract

This paper investigates the applicability of tubebased robust MPC to control the blood glucose concentration in type 1 diabetic subjects through simulation studies. An existing, widely used metabolic simulation model is adopted and extended with several physiologically motivated uncertainties to account for the underlying intra–patient variability observed in real patients. Results on a virtual subject show that robustness to uncertainties can be improved and the risk of hypoglycemia reduced, without increasing the online computational demands compared to the conventional model predictive control approach.

Originalsprache	Englisch
Titel	52nd IEEE Conference on Decision and Control (CDC)
Seiten	2084-2089
Seitenumfang	6
DOIs	https://doi.org/10.1109/CDC.2013.6760189
Publikationsstatus	Veröffentlicht - Dez. 2013

Publikationsreihe

Name	Proceedings of the IEEE Conference on Decision and Control
ISSN (Print)	0743-1546
ISSN (elektronisch)	2576-2370

UN SDGs

Dieser Output leistet einen Beitrag zu folgendem(n) Ziel(en) für nachhaltige Entwicklung

SDG 3 – Gute Gesundheit und Wohlergehen

Wissenschaftszweige

203 Maschinenbau
202034 Regelungstechnik
202012 Elektrische Messtechnik
206 Medizintechnik
202027 Mechatronik
202003 Automatisierungstechnik
203027 Verbrennungskraftmaschinen
207109 Schadstoffemission

JKU-Schwerpunkte

Mechatronics and Information Processing

Zugriff auf Dokument

10.1109/CDC.2013.6760189

Andere Dateien und Links

Verknüpfung zur Publikation in Scopus

Dieses zitieren

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abstract = "This paper investigates the applicability of tubebased robust MPC to control the blood glucose concentration in type 1 diabetic subjects through simulation studies. An existing, widely used metabolic simulation model is adopted and extended with several physiologically motivated uncertainties to account for the underlying intra–patient variability observed in real patients. Results on a virtual subject show that robustness to uncertainties can be improved and the risk of hypoglycemia reduced, without increasing the online computational demands compared to the conventional model predictive control approach.",

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