Modeling, identification and state estimation for artificial pancreas. Part II

GODOY, JOSÉ LUIS; ABUIN, PABLO; RIVADENEIRA, PABLO S.; GONZÁLEZ, ALEJANDRO HERNÁN

Congreso; XXVIº Congreso Argentino de Control Automático; 2018

Type 1 Diabetes is currently controlled by manymodel-based advanced control techniques, embedded in what isknown as artificial pancreas (AP). In this context, the problemof the identification and state estimation is crucial to accountfor the effect of measured, partially-measured and unmeasuredinputs/disturbances. In this work, a diabetic patient minimalmodel with long-term validity is proposed and, based on thismodel, several identifications are performed. Furthermore, andno less important, different estimators are designed to obtainthe states in several scenarios, including unannounced meals andmeasurements errors in the glycemia and insulin injections. Theproperties/benefits of the modeling, identification and estimationare discussed in detail in this two-parts work: Part I describes themodel and the identification, while Part II is devoted to study theestimation problem. Both parts includes several simulation results- based on real and virtual patient data - which demonstrate thepros and cons of the proposed strategies.