5 research outputs found
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