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

Institute for Machine Intelligence

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

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.

Original language	English
Title of host publication	52nd IEEE Conference on Decision and Control (CDC)
Pages	2084-2089
Number of pages	6
DOIs	https://doi.org/10.1109/CDC.2013.6760189
Publication status	Published - Dec 2013

Publication series

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Fields of science

203 Mechanical Engineering
202034 Control engineering
202012 Electrical measurement technology
206 Medical Engineering
202027 Mechatronics
202003 Automation
203027 Internal combustion engines
207109 Pollutant emission

JKU Focus areas

Mechatronics and Information Processing

Access to Document

10.1109/CDC.2013.6760189

Cite this

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title = "Robust tube-based predictive control of blood glucose concentration in type 1 diabetes",

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.",

author = "Harald Kirchsteiger and \{Del Re\}, Luigi",

year = "2013",

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doi = "10.1109/CDC.2013.6760189",

language = "English",

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series = "Proceedings of the IEEE Conference on Decision and Control",

pages = "2084--2089",

booktitle = "52nd IEEE Conference on Decision and Control (CDC)",

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Robust tube-based predictive control of blood glucose concentration in type 1 diabetes. / Kirchsteiger, Harald; Del Re, Luigi.
52nd IEEE Conference on Decision and Control (CDC). 2013. p. 2084-2089 6760189 (Proceedings of the IEEE Conference on Decision and Control).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

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AU - Del Re, Luigi

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N2 - 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.

AB - 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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U2 - 10.1109/CDC.2013.6760189

DO - 10.1109/CDC.2013.6760189

M3 - Conference proceedings

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T3 - Proceedings of the IEEE Conference on Decision and Control

SP - 2084

EP - 2089

BT - 52nd IEEE Conference on Decision and Control (CDC)

ER -

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

Abstract

Publication series

UN SDGs

Fields of science

JKU Focus areas

Access to Document

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Cite this