Paper Info
Title
Embedded Model Predictive Control for a Wearable Artificial Pancreas
Abstract
While artificial pancreas (AP) systems are expected to improve the quality of life among people with type 1 diabetes mellitus (T1DM), the design of convenient systems that optimize the user experience, especially for those with active lifestyles, such as children and adolescents, still remains an open research question. In this brief, we introduce an embeddable design and implementation of model predictive control (MPC) of AP systems for people with T1DM that significantly reduces the weight and on-body footprint of the AP system. The embeddable controller is based on a zone MPC (ZMPC) that has been evaluated in multiple clinical studies. The proposed embedded ZMPC features a simpler design of the periodic safe zone in the cost function and the utilization of state-of-the-art alternating minimization algorithms for solving the convex programing problems inherent to MPC with linear models subject to convex constraints. Offline closed-loop data generated by the FDA-accepted UVA/Padova simulator are used to select an optimization algorithm and the corresponding tuning parameters. Through hardware-in-the-loop <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in silico</italic> results on a limited-resource Arduino Zero (Feather M0) platform, we demonstrate the potential of the proposed embedded MPC. In spite of resource limitations, our embedded ZMPC manages to achieve a comparable performance of that of the full-version ZMPC implemented in a 64-bit desktop for scenarios with/without meal-disturbance compensations. Metrics for performance comparison included the median percent time in the euglycemic ([70, 180] mg/dL range) of 84.3% versus 83.1% for announced meals, with an equivalence test yielding <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p=0.0013$ </tex-math></inline-formula> and 66.2% versus 66.0% for unannounced meals with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p=0.0028$ </tex-math></inline-formula> .
Year
DOI
Venue
2020
10.1109/TCST.2019.2939122
IEEE Transactions on Control Systems Technology
Keywords
DocType
Volume
Insulin,Optimization,Sugar,Biomedical monitoring,Predictive control,Diabetes,Pancreas
Journal
28
Issue
ISSN
Citations 
6
1063-6536
3
PageRank 
References 
Authors
0.53
8
6
Name
Order
Citations
PageRank
Ankush Chakrabarty130.53
Elizabeth Healey230.53
Dawei Shi331226.03
Stamatina Zavitsanou481.64
Francis J Doyle524445.10
Eyal Dassau6386.55