A Nonlinear Model Predictive Control Strategy for Glucose Control in People with Type 1 Diabetes

Dimitri Boiroux; John Bagterp Jørgensen

doi:10.1016/j.ifacol.2018.11.644

A Nonlinear Model Predictive Control Strategy for Glucose Control in People with Type 1 Diabetes

Dimitri Boiroux^*, John Bagterp Jørgensen

^*Corresponding author for this work

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Abstract

In this paper, we evaluate the closed-loop performance of a control algorithm for the treatment of type 1 diabetes (T1D) identified from prior continuous glucose monitor (CGM) data. The control algorithm is based on nonlinear model predictive control (NMPC). At each iteration, we solve an optimal control problem (OCP) using a sequential quadratic programming algorithm with multiple shooting and sensitivity computation. The control algorithm uses a physiological model of T1D to predict future blood glucose (BG) concentrations. The T1D physiological model takes into account the dynamics between subcutaneously administered insulin and blood glucose, the contribution of meal absorption and the lag and noise of CGM measurements. The model parameters have been identified using prior data. Numerical simulations on 10 patients show that the NMPC algorithm is safe and is able to optimize the insulin delivery in patients with T1D.

Original language	English
Book series	I F A C Workshop Series
Volume	51
Issue number	27
Pages (from-to)	192-197
ISSN	1474-6670
DOIs	https://doi.org/10.1016/j.ifacol.2018.11.644
Publication status	Published - 2018

Keywords

Continuous-discrete extended Kalman filter
Diabetes technology
Nonlinear Model Predictive Control
Type 1 diabetes

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This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.ifacol.2018.11.644

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title = "A Nonlinear Model Predictive Control Strategy for Glucose Control in People with Type 1 Diabetes",

abstract = "In this paper, we evaluate the closed-loop performance of a control algorithm for the treatment of type 1 diabetes (T1D) identified from prior continuous glucose monitor (CGM) data. The control algorithm is based on nonlinear model predictive control (NMPC). At each iteration, we solve an optimal control problem (OCP) using a sequential quadratic programming algorithm with multiple shooting and sensitivity computation. The control algorithm uses a physiological model of T1D to predict future blood glucose (BG) concentrations. The T1D physiological model takes into account the dynamics between subcutaneously administered insulin and blood glucose, the contribution of meal absorption and the lag and noise of CGM measurements. The model parameters have been identified using prior data. Numerical simulations on 10 patients show that the NMPC algorithm is safe and is able to optimize the insulin delivery in patients with T1D.",

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KW - Diabetes technology

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KW - Type 1 diabetes

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A Nonlinear Model Predictive Control Strategy for Glucose Control in People with Type 1 Diabetes

Abstract

Keywords

UN SDGs

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