Comparison of several data‐driven non‐linear system identification methods on a simplified glucoregulatory system example. Issue 17 (1st November 2014) - Marconato, Anna; Schoukens, Maarten; Tiels, Koen; Widanage, Widanalage Dhammika; Abu‐Rmileh, Amjad; Schoukens, Johan
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.