Volume 8 Number 4 (Oct. 2018)
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IJBBB 2018 Vol.8(4): 195-201 ISSN: 2010-3638
doi: 10.17706/ijbbb.2018.8.4.195-201

A New Model Predictive Control for the Artificial Pancreas

Mauro Capocelli, Luca De Santis, Vincenzo Piemonte
Abstract—Closed-loop insulin delivery system have led to significant improvement in the quality of life of subject with diabetes and are challenging to overcome the barrier of hypoglycemia, the most frequent complication of insulin therapy. The reliability of the system, composed by a computer algorithm, a glucose sensor and an insulin infusion device, depends on the knowledge and predictor capacity of the physiology of blood glucose regulation. This paper describes the physical-mathematical fundamentals and the most important results of a new three-compartmental model. The model includes exogenous insulin injected in subcutaneous tissue with local degradation, three explicit delays and three influencing physiologically-based parameters controlling the regulatory system. The parameters have been calculated through the simulation of actual clinical data and, therefore, implemented into the mathematical model to successfully simulate the clinical data obtained at Campus Biomedico in the normal-life regulation (1 day and 4 days) of diabetic patients. The estimated model parameters were physiological meaningful and provided insights on the subject's dysfunction.

Index Terms—Artificial pancreas, closed loop control, predictive control model, compartimental model.

Mauro Capocelli is with Unit of Process Engineering, Department of Engineering, UniversitàCampus Bio-Medico di Roma, via Álvaro del Portillo 21, 00128 Rome, Italy.
Luca De Santis, Vincenzo Piemonte are with Unit of Chemico-physical fundamentals in Chemical Engineering, Università Campus Bio-Medico di Roma, via Álvaro del Portillo 21, 00128 Rome, Italy (email: v.piemonte@unicampus.it).

Cite: Mauro Capocelli, Luca De Santis, Vincenzo Piemonte, "A New Model Predictive Control for the Artificial Pancreas," International Journal of Bioscience, Biochemistry and Bioinformatics vol. 8, no. 4, pp. 195-201, 2018.

General Information

ISSN: 2010-3638
Frequency: Bimonthly (2011-2015); Quarterly (Since 2016)
DOI: 10.17706/IJBBB
Editor-in-Chief: Prof. Ebtisam Heikal 
Abstracting/ Indexing: Electronic Journals Library, Chemical Abstracts Services (CAS), Google Scholar, and ProQuest.
E-mail: ijbbb@iap.org
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