Linear Matrix Inequality-based Robust Controller design for Type-1 Diabetes Model

This paper investigates the capabilities of a sophisticate d robust nonlinear controller designed directly for a widely known and used high-order non linear type 1 diabetes (T1DM) model to lessen the dependency from patient compliance and t o answer practical requirements such as avoiding hypoglycaemia. The resulting controller c an perform adequately in nominal conditions, but expected to keep this performance even in ex treme situations, e.g. high carbohydrate intake, rejecting hypoglycaemic episodes

Comparison of sigma-point filters for state estimation of diabetes models

In physiological control there is a need to esti- mate signals that cannot be measured directly. Burdened by measurement noise and unknown disturbances this proves to be challenging, since the models are usually highly nonlinear. Sigma- point filters could represent an adequate choice to overcome this problem. The paper investigates the applicability of several different versions of sigma-point filters for the Artificial Pancreas problem on the widely used Cambridge (Hovorka)-model

Analyzing of a Novel Time Delay Diabetes Model

Nowadays, the requirements in the high quality managing and mathematical modeling of diabetes are increas- ing. Due to the several types of this chronic disease the problem represents a challenging task that was mostly covered in the literature for type 1 diabetes while examining algorithms for artificial pancreas. The aim of the current paper is to analyze a recently developed novel time-delay diabetes model elaborated for both type 1 and type 2 diabetes mellitus; hence, to handle mixed state of these diseases (double diabetes) as well. Control theoretical characteristics are investigated followed by parametric sensitivity analysis for both type 1 and type 2 cases. Finally, the model is compared with the well-known and widely used Cambridge (Hovorka)-model under different simulation scenarios