Nonlinear Model Predictive Control and System Identification for a Dual-hormone Artificial Pancreas

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In 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.

OriginalsprogEngelsk
TidsskriftIFAC-PapersOnLine
Vol/bind55
Udgave nummer7
Sider (fra-til)915-921
ISSN2405-8963
DOI
StatusUdgivet - 2022
Begivenhed13th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems, DYCOPS 2022 - Busan, Sydkorea
Varighed: 14 jun. 202217 jun. 2022

Konference

Konference13th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems, DYCOPS 2022
LandSydkorea
ByBusan
Periode14/06/202217/06/2022

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