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

Asbjørn Thode Reenberg; Tobias K.S. Ritschel; Emilie B. Lindkvist; Christian Laugesen; Jannet Svensson; Ajenthen G. Ranjan; Kirsten Nørgaard; John Bagterp Jørgensen

doi:10.1016/j.ifacol.2022.07.561

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

Asbjørn Thode Reenberg, Tobias K.S. Ritschel, Emilie B. Lindkvist, Christian Laugesen, Jannet Svensson, Ajenthen G. Ranjan, Kirsten Nørgaard, John Bagterp Jørgensen^*

^*Corresponding author for this work

Steno Diabetes Center Copenhagen

5 Citations (Scopus)

Abstract

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.

Original language	English
Journal	IFAC-PapersOnLine
Volume	55
Issue number	7
Pages (from-to)	915-921
Number of pages	7
ISSN	2405-8963
DOIs	https://doi.org/10.1016/j.ifacol.2022.07.561
Publication status	Published - 2022
Event	13th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems, DYCOPS 2022 - Busan, Korea, Republic of Duration: 14 Jun 2022 → 17 Jun 2022

Conference

Conference	13th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems, DYCOPS 2022
Country/Territory	Korea, Republic of
City	Busan
Period	14/06/2022 → 17/06/2022

Keywords

Artificial Pancreas
Model Predictive Control
Optimal Control
Physiological modeling
System Identification

Access to Document

10.1016/j.ifacol.2022.07.561

Cite this

@inproceedings{9338d1d09377469585500fd1bef4d250,

title = "Nonlinear Model Predictive Control and System Identification for a Dual-hormone Artificial Pancreas",

abstract = "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.",

keywords = "Artificial Pancreas, Model Predictive Control, Optimal Control, Physiological modeling, System Identification",

author = "Reenberg, {Asbj{\o}rn Thode} and Ritschel, {Tobias K.S.} and Lindkvist, {Emilie B.} and Christian Laugesen and Jannet Svensson and Ranjan, {Ajenthen G.} and Kirsten N{\o}rgaard and J{\o}rgensen, {John Bagterp}",

note = "Publisher Copyright: {\textcopyright} 2022 Elsevier B.V.. All rights reserved.; 13th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems, DYCOPS 2022 ; Conference date: 14-06-2022 Through 17-06-2022",

year = "2022",

doi = "10.1016/j.ifacol.2022.07.561",

language = "English",

volume = "55",

pages = "915--921",

journal = "IFAC-PapersOnLine",

issn = "2405-8963",

publisher = "IFAC Secretariat",

number = "7",

}

TY - GEN

T1 - Nonlinear Model Predictive Control and System Identification for a Dual-hormone Artificial Pancreas

AU - Reenberg, Asbjørn Thode

AU - Ritschel, Tobias K.S.

AU - Lindkvist, Emilie B.

AU - Laugesen, Christian

AU - Svensson, Jannet

AU - Ranjan, Ajenthen G.

AU - Nørgaard, Kirsten

AU - Jørgensen, John Bagterp

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - Artificial Pancreas

KW - Model Predictive Control

KW - Optimal Control

KW - Physiological modeling

KW - System Identification

UR - http://www.scopus.com/inward/record.url?scp=85137068983&partnerID=8YFLogxK

U2 - 10.1016/j.ifacol.2022.07.561

DO - 10.1016/j.ifacol.2022.07.561

M3 - Conference article

AN - SCOPUS:85137068983

SN - 2405-8963

VL - 55

SP - 915

EP - 921

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

IS - 7

T2 - 13th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems, DYCOPS 2022

Y2 - 14 June 2022 through 17 June 2022

ER -

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

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this