Model predictive control for type 1 diabetes based on personalized linear time-varying subject model consisting of both insulin and meal inputs: In Silico evaluation

Qian Wang; Jinyu Xie; Peter Molenaar; Jan Ulbrecht

doi:10.1109/ACC.2015.7172245

Model predictive control for type 1 diabetes based on personalized linear time-varying subject model consisting of both insulin and meal inputs: In Silico evaluation

Qian Wang
, Jinyu Xie
, Peter Molenaar
, Jan Ulbrecht

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Scopus citations

Abstract

An essential component of insulin therapy for type 1 diabetes involves the prediction of blood glucose levels as function of exogenous perturbations such as insulin dose and meal intake. Based on the authors' previously developed patient-specific linear time-varying model for glucose dynamics consisting of both insulin and meal inputs, this paper develops a model predictive control for determining the optimal insulin delivery to regulate blood glucose in euglycemic range. Evaluation of the developed controller using the UVa/Padova simulator shows promising results. In addition, results in this paper show the importance of explicitly including the meal intake in the regression model, which was often lacking in the existing empirical subject-model based control.

Original language	English (US)
Title of host publication	ACC 2015 - 2015 American Control Conference
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5782-5787
Number of pages	6
ISBN (Electronic)	9781479986842
DOIs	https://doi.org/10.1109/ACC.2015.7172245
State	Published - Jul 28 2015
Event	2015 American Control Conference, ACC 2015 - Chicago, United States Duration: Jul 1 2015 → Jul 3 2015

Publication series

Name	Proceedings of the American Control Conference
Volume	2015-July
ISSN (Print)	0743-1619

Other

Other	2015 American Control Conference, ACC 2015
Country/Territory	United States
City	Chicago
Period	7/1/15 → 7/3/15

UN SDGs

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

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering

Access to Document

10.1109/ACC.2015.7172245

Cite this

Wang, Q., Xie, J., Molenaar, P., & Ulbrecht, J. (2015). Model predictive control for type 1 diabetes based on personalized linear time-varying subject model consisting of both insulin and meal inputs: In Silico evaluation. In ACC 2015 - 2015 American Control Conference (pp. 5782-5787). Article 7172245 (Proceedings of the American Control Conference; Vol. 2015-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACC.2015.7172245

Wang, Qian ; Xie, Jinyu ; Molenaar, Peter et al. / Model predictive control for type 1 diabetes based on personalized linear time-varying subject model consisting of both insulin and meal inputs : In Silico evaluation. ACC 2015 - 2015 American Control Conference. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 5782-5787 (Proceedings of the American Control Conference).

@inproceedings{bd0aa6e730554e42b02b0e886da2ce6e,

title = "Model predictive control for type 1 diabetes based on personalized linear time-varying subject model consisting of both insulin and meal inputs: In Silico evaluation",

abstract = "An essential component of insulin therapy for type 1 diabetes involves the prediction of blood glucose levels as function of exogenous perturbations such as insulin dose and meal intake. Based on the authors' previously developed patient-specific linear time-varying model for glucose dynamics consisting of both insulin and meal inputs, this paper develops a model predictive control for determining the optimal insulin delivery to regulate blood glucose in euglycemic range. Evaluation of the developed controller using the UVa/Padova simulator shows promising results. In addition, results in this paper show the importance of explicitly including the meal intake in the regression model, which was often lacking in the existing empirical subject-model based control.",

author = "Qian Wang and Jinyu Xie and Peter Molenaar and Jan Ulbrecht",

note = "Publisher Copyright: {\textcopyright} 2015 American Automatic Control Council.; 2015 American Control Conference, ACC 2015 ; Conference date: 01-07-2015 Through 03-07-2015",

year = "2015",

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day = "28",

doi = "10.1109/ACC.2015.7172245",

language = "English (US)",

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Wang, Q, Xie, J, Molenaar, P & Ulbrecht, J 2015, Model predictive control for type 1 diabetes based on personalized linear time-varying subject model consisting of both insulin and meal inputs: In Silico evaluation. in ACC 2015 - 2015 American Control Conference., 7172245, Proceedings of the American Control Conference, vol. 2015-July, Institute of Electrical and Electronics Engineers Inc., pp. 5782-5787, 2015 American Control Conference, ACC 2015, Chicago, United States, 7/1/15. https://doi.org/10.1109/ACC.2015.7172245

Model predictive control for type 1 diabetes based on personalized linear time-varying subject model consisting of both insulin and meal inputs: In Silico evaluation. / Wang, Qian; Xie, Jinyu; Molenaar, Peter et al.
ACC 2015 - 2015 American Control Conference. Institute of Electrical and Electronics Engineers Inc., 2015. p. 5782-5787 7172245 (Proceedings of the American Control Conference; Vol. 2015-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Ulbrecht, Jan

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N2 - An essential component of insulin therapy for type 1 diabetes involves the prediction of blood glucose levels as function of exogenous perturbations such as insulin dose and meal intake. Based on the authors' previously developed patient-specific linear time-varying model for glucose dynamics consisting of both insulin and meal inputs, this paper develops a model predictive control for determining the optimal insulin delivery to regulate blood glucose in euglycemic range. Evaluation of the developed controller using the UVa/Padova simulator shows promising results. In addition, results in this paper show the importance of explicitly including the meal intake in the regression model, which was often lacking in the existing empirical subject-model based control.

AB - An essential component of insulin therapy for type 1 diabetes involves the prediction of blood glucose levels as function of exogenous perturbations such as insulin dose and meal intake. Based on the authors' previously developed patient-specific linear time-varying model for glucose dynamics consisting of both insulin and meal inputs, this paper develops a model predictive control for determining the optimal insulin delivery to regulate blood glucose in euglycemic range. Evaluation of the developed controller using the UVa/Padova simulator shows promising results. In addition, results in this paper show the importance of explicitly including the meal intake in the regression model, which was often lacking in the existing empirical subject-model based control.

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Wang Q, Xie J, Molenaar P, Ulbrecht J. Model predictive control for type 1 diabetes based on personalized linear time-varying subject model consisting of both insulin and meal inputs: In Silico evaluation. In ACC 2015 - 2015 American Control Conference. Institute of Electrical and Electronics Engineers Inc. 2015. p. 5782-5787. 7172245. (Proceedings of the American Control Conference). doi: 10.1109/ACC.2015.7172245

Model predictive control for type 1 diabetes based on personalized linear time-varying subject model consisting of both insulin and meal inputs: In Silico evaluation

Abstract

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