Nonlinear MPC for Insulin Titration of Type 2 Diabetes

Joachim Melkær Midtgaard; Kasper Bruhn; Michael Siggaard Jørgensen; Tristan Grusgaard Johnasen; Kasper Kjærulff Grønkjær; Faiyaz Alvi Ahmed; Mohamad Al Ahdab; John-Josef Leth

doi:10.1016/j.ifacol.2024.09.006

Nonlinear MPC for Insulin Titration of Type 2 Diabetes

Joachim Melkær Midtgaard^*, Kasper Bruhn, Michael Siggaard Jørgensen, Tristan Grusgaard Johnasen, Kasper Kjærulff Grønkjær, Faiyaz Alvi Ahmed, Mohamad Al Ahdab, John-Josef Leth

^*Corresponding author for this work

Research output: Contribution to journal › Conference article in Journal › Research › peer-review

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Abstract

This paper explores the development and application of both linear and nonlinear model predictive control (MPC) strategies for insulin titration in type 2 diabetes (T2D) subjects. By utilizing daily blood glucose measurements, alongside information on insulin injections and meal intake from the previous day, we adjust the insulin sensitivity parameter of the internal model of the controller. This adjustment is based on the steady-state glucose error between the internal model and the plant model. The performance of these strategies was assessed using a high-fidelity T2D model, demonstrating their potential in enhancing the management of T2D.

Original language	English
Book series	IFAC-PapersOnLine
Volume	58
Issue number	18
Pages (from-to)	35-40
Number of pages	6
ISSN	1474-6670
DOIs	https://doi.org/10.1016/j.ifacol.2024.09.006
Publication status	Published - 2024
Event	8th IFAC Conference on Nonlinear Model Predictive Control NMPC 2024 - Kyoto University, Kyoto, Japan Duration: 21 Aug 2024 → 24 Aug 2024 Conference number: 8 https://nmpc2024.org/

Conference

Conference	8th IFAC Conference on Nonlinear Model Predictive Control NMPC 2024
Number	8
Location	Kyoto University
Country/Territory	Japan
City	Kyoto
Period	21/08/2024 → 24/08/2024
Internet address	https://nmpc2024.org/

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title = "Nonlinear MPC for Insulin Titration of Type 2 Diabetes",

abstract = "This paper explores the development and application of both linear and nonlinear model predictive control (MPC) strategies for insulin titration in type 2 diabetes (T2D) subjects. By utilizing daily blood glucose measurements, alongside information on insulin injections and meal intake from the previous day, we adjust the insulin sensitivity parameter of the internal model of the controller. This adjustment is based on the steady-state glucose error between the internal model and the plant model. The performance of these strategies was assessed using a high-fidelity T2D model, demonstrating their potential in enhancing the management of T2D.",

author = "Midtgaard, {Joachim Melk{\ae}r} and Kasper Bruhn and J{\o}rgensen, {Michael Siggaard} and Johnasen, {Tristan Grusgaard} and Gr{\o}nkj{\ae}r, {Kasper Kj{\ae}rulff} and Ahmed, {Faiyaz Alvi} and Ahdab, {Mohamad Al} and John-Josef Leth",

year = "2024",

doi = "10.1016/j.ifacol.2024.09.006",

language = "English",

volume = "58",

pages = "35--40",

journal = "IFAC-PapersOnLine",

issn = "1474-6670",

publisher = "Elsevier",

number = "18",

note = "8th IFAC Conference on Nonlinear Model Predictive Control NMPC 2024, IFAC 8 ; Conference date: 21-08-2024 Through 24-08-2024",

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TY - GEN

T1 - Nonlinear MPC for Insulin Titration of Type 2 Diabetes

AU - Midtgaard, Joachim Melkær

AU - Bruhn, Kasper

AU - Jørgensen, Michael Siggaard

AU - Johnasen, Tristan Grusgaard

AU - Grønkjær, Kasper Kjærulff

AU - Ahmed, Faiyaz Alvi

AU - Ahdab, Mohamad Al

AU - Leth, John-Josef

N1 - Conference code: 8

PY - 2024

Y1 - 2024

N2 - This paper explores the development and application of both linear and nonlinear model predictive control (MPC) strategies for insulin titration in type 2 diabetes (T2D) subjects. By utilizing daily blood glucose measurements, alongside information on insulin injections and meal intake from the previous day, we adjust the insulin sensitivity parameter of the internal model of the controller. This adjustment is based on the steady-state glucose error between the internal model and the plant model. The performance of these strategies was assessed using a high-fidelity T2D model, demonstrating their potential in enhancing the management of T2D.

AB - This paper explores the development and application of both linear and nonlinear model predictive control (MPC) strategies for insulin titration in type 2 diabetes (T2D) subjects. By utilizing daily blood glucose measurements, alongside information on insulin injections and meal intake from the previous day, we adjust the insulin sensitivity parameter of the internal model of the controller. This adjustment is based on the steady-state glucose error between the internal model and the plant model. The performance of these strategies was assessed using a high-fidelity T2D model, demonstrating their potential in enhancing the management of T2D.

U2 - 10.1016/j.ifacol.2024.09.006

DO - 10.1016/j.ifacol.2024.09.006

M3 - Conference article in Journal

SN - 1474-6670

VL - 58

SP - 35

EP - 40

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

IS - 18

T2 - 8th IFAC Conference on Nonlinear Model Predictive Control NMPC 2024

Y2 - 21 August 2024 through 24 August 2024

ER -

Nonlinear MPC for Insulin Titration of Type 2 Diabetes

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