Model predictive control with integral action for artificial pancreas

被引：40

作者：

Incremona, Gian Paolo ^{[1
]}

Messori, Mirko ^{[2
]}

Toffanin, Chiara ^{[2
]}

Cobelli, Claudio ^{[3
]}

Magni, Lalo ^{[4
]}

机构：

[1] Politecn Milan, Dipartimento Elettron Informaz & Bioingn, Piazza Leonardo da Vinci 32, I-20133 Milan, Italy

[2] Dept Ind & Informat Engn, Via Ferrata 5, I-27100 Pavia, Italy

[3] Univ Padua, Dept Informat Engn, Via Gradenigo 6-A, I-35131 Padua, Italy

[4] Univ Pavia, Dept Civil Engn & Architecture, Via Ferrata 1, I-27100 Pavia, Italy

来源：

CONTROL ENGINEERING PRACTICE | 2018年 / 77卷

关键词：

Biomedical control; Biomedical system; Predictive control; Integral action; System identification; LOOP GLUCOSE CONTROL; TYPE-1 DIABETES SIMULATOR; FREE-LIVING CONDITIONS; BLOOD-GLUCOSE; RANDOMIZED CROSSOVER; INSULIN DELIVERY; SYSTEM; IDENTIFICATION; ADULTS; TRIAL;

D O I：

10.1016/j.conengprac.2018.05.006

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A Model Predictive Control (MPC) approach with integral action, called Integral MPC (IMPC), for Artificial Pancreas systems is proposed. IMPC ensures beneficial effects in terms of regulation to target in presence of disturbances and model uncertainties. The proposed approach exploits individualized models identified by Constrained Optimization (CO) described in Messori et al. (2016). In order to assess the proposed IMPC in comparison with a previously published MPC, in silico experiments are carried out on realistic scenarios performed on the 100 virtual patients of the UVA/PADOVA simulator.

引用

页码：86 / 94

页数：9

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