Title: Accelerated model predictive controller for artificial pancreas

Authors: Mohamed El Hachimi; Abdelhakim Ballouk; Ilyass Khelafa; Abdennaceur Baghdad

Addresses: Laboratory of Electronics, Energy, Automatic and Data Processing (LEEA&TI), FST-Mohammedia University, Hassan II of Casablanca, BP 146, 20650 Mohammedia, Morocco ' Laboratory of Electronics, Energy, Automatic and Data Processing (LEEA&TI), FST-Mohammedia University, Hassan II of Casablanca, BP 146, 20650 Mohammedia, Morocco ' Laboratory of Electronics, Energy, Automatic and Data Processing (LEEA&TI), FST-Mohammedia University, Hassan II of Casablanca, BP 146, 20650 Mohammedia, Morocco ' Laboratory of Electronics, Energy, Automatic and Data Processing (LEEA&TI), FST-Mohammedia University, Hassan II of Casablanca, BP 146, 20650 Mohammedia, Morocco

Abstract: This work consists of a contribution to the artificial pancreas (AP) development by introducing new techniques of control based on an acceleration of reference tracking by using a variable penalisation of the cost function instead of fixed and arbitrary penalisation, two new functions of the weighting factors are introduced in the formulation of the control algorithm. This method allows a rapid rejection of meal disturbance, a reduction of glucose peak and a complete avoidance of hypoglycaemia. The developed controller performances are evaluated in silico test which is equivalent to animal test using the UVa/Padova simulator.

Keywords: artificial pancreas; AP; reference tracking; weighting factors; control algorithm; disturbance; hypoglycaemia.

DOI: 10.1504/IJMIC.2018.095335

International Journal of Modelling, Identification and Control, 2018 Vol.30 No.3, pp.229 - 238

Received: 15 Jun 2017
Accepted: 02 Oct 2017
Published online: 03 Oct 2018 *

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