Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.bspc.2017.12.002
Title: Evaluation of an artificial pancreas in in silico patients with online-tuned internal model control
Authors: Bhattacharjee A.
Easwaran A.
Leow M.K.-S. 
Cho N.
Keywords: Artificial pancreas
Internal model control
Recursive least squares
Type 1 diabetes mellitus
Volterra model
Issue Date: 2018
Publisher: Elsevier Ltd
Citation: Bhattacharjee A., Easwaran A., Leow M.K.-S., Cho N. (2018). Evaluation of an artificial pancreas in in silico patients with online-tuned internal model control. Biomedical Signal Processing and Control 41 : 198 - 209. ScholarBank@NUS Repository. https://doi.org/10.1016/j.bspc.2017.12.002
Abstract: A fully-automated controller in the artificial pancreas (AP) system designed to regulate blood glucose concentration can give better lifestyle to a type 1 diabetic patient. This paper deals with evaluating the benefit of fully-automated online-tuned controller for the AP system over offline-tuned and semi-automated controller based on internal model control (IMC) strategy. The online-tuned controller is fully-automatic in the sense that it can automatically deal with intra- and inter-patient variabilities and compensate for unannounced meal disturbances without any prior knowledge of patient parameters, patient specific characteristics or patient specific input-output data. A data driven Volterra model of patients is used to design IMC algorithms. For online-tuned controller, the Volterra kernels of the model are computed online by recursive least squares algorithm. The IMC algorithms are evaluated using different scenarios in the UVA/Padova metabolic simulator for validation, comparison with a fully-automatic zone model predictive controller and robustness analysis. Unlike offline-tuned IMC and semi-automated IMC, the online-tuned IMC in the AP system performs satisfactorily for every patient condition without patients' intervention. Experimental results show that the online-tuned IMC compensates unannounced meal disturbances with low frequency of hypoglycemic events and most importantly, with low insulin infusion even with variations in insulin sensitivity, in the presence of irregular amounts of meal disturbances at random times, and in the presence of very high noise levels in the sensors and actuators. Patients experience hypoglycemia 0.46%, 1.01% and 20% of the time using online-tuned, offline-tuned and semi-automated IMC respectively when the insulin sensitivity is increased by +20%. 2017 Elsevier Ltd
Source Title: Biomedical Signal Processing and Control
URI: https://scholarbank.nus.edu.sg/handle/10635/177584
ISSN: 17468094
DOI: 10.1016/j.bspc.2017.12.002
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