Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/89311
Title: Evaluation of an artificial pancreas in in silico patients with online-tuned internal model control
Authors: Cho, Namjoon
Bhattacharjee, Arpita
Easwaran, Arvind
Leow, Melvin Khee-Shing
Keywords: Type 1 Diabetes Mellitus
Artificial Pancreas
Issue Date: 2017
Source: 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.
Series/Report no.: Biomedical Signal Processing and Control
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%.
URI: https://hdl.handle.net/10356/89311
http://hdl.handle.net/10220/44883
ISSN: 1746-8094
DOI: 10.1016/j.bspc.2017.12.002
Rights: © 2017 Elsevier Ltd. This is the author created version of a work that has been peer reviewed and accepted for publication by Biomedical Signal Processing and Control, Elsevier Ltd. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1016/j.bspc.2017.12.002].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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