Comparison of several data-driven nonlinear system identification methods on a simplified glucoregulatory system example
For researchers developing artificial pancreas systems, this comparison identifies suitable nonlinear models for glucose regulation, though it is an incremental application of existing methods to a specific problem.
The paper compares several data-driven nonlinear system identification methods on a simplified glucoregulatory system model for artificial pancreas development. Block-oriented and state-space models accurately simulate patient behavior, with some being simple enough for model-based controller implementation.
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.