A robust fixed point transformation-based approach for type 1 diabetes control

Hypoglycaemia-free artificial pancreas project

Driving blood glycaemia from hyperglycaemia to euglycaemia as fast as possible while avoiding hypoglycaemia is a major problem for decades for type-1 diabetes and is solved in this study. A control algorithm is designed that guaranties hypoglycaemia avoidance for the first time both from the theory of positive systems point of view and from the most pragmatic clinical practice. The solution consists of a state feedback control law that computes the required hyperglycaemia correction bolus in real-time to safely steer glycaemia to the target. A rigorous proof is given that shows that the control-law respects the positivity of the control and of the glucose concentration error: as a result, no hypoglycaemic episode occurs. The so-called hypo-free strategy control is tested with all the UVA/Padova T1DM simulator patients (i.e. ten adults, ten adolescents, and ten children) during a fasting-night scenario and in a hybrid closed-loop scenario including three meals. The theoretical results are assessed by the simulations on a large cohort of virtual patients and encourage clinical trials.

Nonlinear Model Predictive Control Using Robust Fixed Point Transformation-Based Phenomena for Controlling Tumor Growth

In this paper a novel control strategy is introduced in order to create optimal dosage profiles for individualized cancer treatment. This approach uses Nonlinear Model Predictive Control to construct optimal dosage protocols in conjunction with Robust Fixed Point Transformations which hinders the negative effect of inherent model uncertainties and measurement disturbances. The results are validated by extensive simulation on the proposed control algorithm from which conclusions were drawn.