Work

Comparison of several data‐driven non‐linear system identification methods on a simplified glucoregulatory system example

Year: 2014

Type: article

Abstract: In this study, several advanced data‐driven non‐linear identification techniques are compared on a specific problem: a simplified glucoregulatory system modelling example. This problem represents a challenge in the development of an artificial pancreas for Type 1 diabetes mellitus treatment, since for this application good non‐linear models are needed to design accurate closed‐loop controllers to regulate the glucose level in the blood. Block‐oriented as well as state‐space models are used to describe both the dynamics and the non‐linear behaviour of the insulin–glucose system, and the advantages and drawbacks of each method are pointed out. The obtained non‐linear models are accurate in simulating the patient's behaviour, and some of them are also sufficiently simple to be considered in the implementation of a model‐based controller to develop the artificial pancreas.

Source: IET Control Theory and Applications

DOI: 10.1049/iet-cta.2014.0534

Authors: Anna Marconato, Maarten Schoukens, Koen Tiels, Widanalage Dhammika Widanage, Amjad Abu‐Rmileh, Johan Schoukens

Institutions: Vrije Universiteit Brussel, University of Warwick, Ben-Gurion University of the Negev

Language: English

Topic: Control Systems and Identification

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: Universitat de Girona, Australian Government, Vlaamse regering

Award: G1654933525