@inproceedings{f621c73f7aa342b883e503d8d8f28e32,
title = "Gaussian process-based model predictive control of blood glucose for patients with type 1 diabetes mellitus",
abstract = "The insulin sensitivity (IS) of the human body changes with a circadian rhythm. This adds to the time-varying feature of the glucose metabolism process and places challenges on the blood glucose (BG) control of patients with Type 1 Diabetes Mellitus. This paper presents a Model Predictive Controller that takes the periodic IS into account, in order to enhance BG control. The future effect of the IS is predicted using a machine learning technique, namely, a customized Gaussian Process (GP), based on historical training data. The training data for the GP is continuously updated during closed-loop control, which enables the control scheme to learn and adapt to intra-individual and inter-individual changes of the circadian IS rhythm. The necessary state information is provided by an Unscented Kalman Filter. The closed-loop performance of the proposed control scheme is evaluated for different scenarios (including fasting, announced meals and skipped meals) through in silico studies on simulation models of G{\"o}ttingen Minipigs.",
keywords = "Artificial Pancreas, Gaussian Process, Model Predictive Control, insulin sensitivity",
author = "Lukas Ortmann and Dawei Shi and Eyal Dassau and Doyle, {Francis J.} and Steffen Leonhardt and Misgeld, {Berno J.E.}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 11th Asian Control Conference, ASCC 2017 ; Conference date: 17-12-2017 Through 20-12-2017",
year = "2018",
month = feb,
day = "7",
doi = "10.1109/ASCC.2017.8287323",
language = "English",
series = "2017 Asian Control Conference, ASCC 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1092--1097",
booktitle = "2017 Asian Control Conference, ASCC 2017",
address = "United States",
}
