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.
Info
Journal Article, 2018
UN SDG Classification
DK Main Research Area
Science/Technology
