From Optimal Design of Experiment to Safe System Identification in Type 2 Diabetes

Abstract

Model-based design of experiment (MBDoE) provides a framework to collect informative data for system identification. However, a parametric and structural mismatch between the design model and the underlying physical system can lead to hazardous experiments in safety critical systems. In this work, we present a method to safely improve system identification based on insights from a model-based optimal experimental design. From a visual inspection of a MBDoE, we select an approximated output curve fulfilling system constraints as a reference for the physical system. To avoid open-loop implementation of the MBDoE, we use our approximated reference together with a reference-tracking controller to collect experimental data in closed-loop. In this type 2 diabetes (T2D) case study, the proposed design method is safe and provides informative experimental data for system identification.