کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6950968 | 1451639 | 2018 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Evaluation of an artificial pancreas in in silico patients with online-tuned internal model control
ترجمه فارسی عنوان
بررسی لوزالمعده مصنوعی در بیماران سیلیکا با کنترل مدل داخلی
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کلمات کلیدی
دیابت نوع 1، لوزالمعده مصنوعی، کنترل مدل داخلی، مدل ولتررا، حداقل مربعات مجاز،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
پردازش سیگنال
چکیده انگلیسی
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%.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Biomedical Signal Processing and Control - Volume 41, March 2018, Pages 198-209
Journal: Biomedical Signal Processing and Control - Volume 41, March 2018, Pages 198-209
نویسندگان
Arpita Bhattacharjee, Arvind Easwaran, Melvin Khee-Shing Leow, Namjoon Cho,