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.

Model predictive control with integral action for artificial pancreas

Incremona, Gian Paolo;Messori, Mirko;Toffanin, Chiara
;
Magni, Lalo
2018

Abstract

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11571/1225125
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