文章基本信息

标题：Nonlinear Model Predictive Control and System Identification for a Dual-hormone Artificial Pancreas
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作者：Asbjørn Thode Reenberg ; Tobias K.S. Ritschel ; Emilie B. Lindkvist 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2022
卷号：55
期号：7
页码：915-921
DOI：10.1016/j.ifacol.2022.07.561
语种：English
出版社：Elsevier
摘要：AbstractIn this work, we present a switching nonlinear model predictive control (NMPC) algorithm for a dual-hormone artificial pancreas (AP), and we use maximum likelihood estimation (MLE) to identify the model parameters. A dual-hormone AP consists of a continuous glucose monitor (CGM), a control algorithm, an insulin pump, and a glucagon pump. The AP is designed with a heuristic to switch between insulin and glucagon as well as state-dependent constraints. We extend an existing glucoregulatory model with glucagon and exercise for simulation, and we use a simpler model for control. We test the AP (NMPC and MLE) using in silico numerical simulations on 50 virtual people with type 1 diabetes. The system is identified for each virtual person based on data generated with the simulation model. The simulations show a mean of 89.3% time in range (3.9–10 mmol/L) and no hypoglycemic events.
关键词：KeywordsArtificial PancreasModel Predictive ControlSystem IdentificationOptimal ControlPhysiological modeling