Parameter estimation in type 1 diabetes models for model-based control applications

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Abstract

In this paper, we discuss the identification of a physiological model describing the glucose-insulin dynamics in people with type 1 diabetes (TID). The identified model has to be applied to nonlinear model predictive control (NMPC). We propose a stochastic model of the glucose-insulin dynamics in TID. Discrete-time glucose data are provided by a continuous glucose monitor (CGM). We use maximum likelihood for parameter estimation, combined with a procedure to compute the gradient of the likelihood function. To test our identification procedure, we generate a virtual population of 10 patients using the Hovorka model and its parameter distribution. We report the estimates of the model parameters, and we use a validation dataset to evaluate the prediction errors for different prediction intervals. Whereas short-term predictions of blood glucose concentrations are consistent among patients, the accuracy of long-term predictions is more subject to inter-patient variability. The results suggest that this method has the potential to be used in NMPC algorithms.

Original languageEnglish
Title of host publicationProceedings of 2019 American Control Conference
PublisherIEEE
Publication date1 Jul 2019
Pages4112-4117
Article number8814933
ISBN (Electronic)9781538679265
Publication statusPublished - 1 Jul 2019
Event2019 American Control Conference - Philadelphia, United States
Duration: 10 Jul 201912 Jul 2019

Conference

Conference2019 American Control Conference
CountryUnited States
CityPhiladelphia
Period10/07/201912/07/2019
SponsorBoeing, GE Global Research, General Motors Co., Mitsubishi Electric Research Laboratory, United Technologies Research Center
SeriesProceedings of the American Control Conference
Volume2019-July
ISSN0743-1619

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