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.
Nonlinear Model Predictive Control and System Identification for a Dual-hormone Artificial Pancreas
2022-01-01
Reenberg , A T , Ritschel , T K S , Lindkvist , E B , Laugesen , C , Svensson , J , Ranjan , A G , Nørgaard , K & Jørgensen , J B 2022 , ' Nonlinear Model Predictive Control and System Identification for a Dual-hormone Artificial Pancreas ' , IFAC-PapersOnLine , vol. 55 , no. 7 , pp. 915-921 . https://doi.org/10.1016/j.ifacol.2022.07.561
Article (Journal)
Electronic Resource
English
DDC: | 629 |
Nonlinear Model Predictive Control and System Identification for a Dual-hormone Artificial Pancreas
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