Comparison of several data-driven nonlinear system identification methods on a simplified glucoregulatory system example

TL;DR

This paper compares various data-driven nonlinear system identification methods on a simplified glucoregulatory system to evaluate their accuracy and practicality for artificial pancreas development.

Contribution

It provides a comparative analysis of block-oriented and state-space models for nonlinear glucose-insulin dynamics in diabetes management.

Findings

Models accurately simulate patient behavior

Some models are simple enough for control implementation

Advantages and drawbacks of each method are discussed

Abstract

In this paper, several advanced data-driven nonlinear identification techniques are compared on a specific problem: a simplified glucoregulatory system modeling example. This problem represents a challenge in the development of an artificial pancreas for T1DM treatment, since for this application good nonlinear 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 nonlinear behavior of the insulin-glucose system, and the advantages and drawbacks of each method are pointed out. The obtained nonlinear models are accurate in simulating the patient's behavior, and some of them are also sufficiently simple to be considered in the implementation of a model-based controller to develop the artificial pancreas.