Model Predictive Control Using the Improved Hovorka Model for the Regulation of Blood Glucose Levels in Type 1 Diabetes

Type 1 diabetes is an autoimmune disease that occurs when the immune system unintentionally attacks and damages $\beta$ cells in the pancreas, reducing the organ’s ability to produce insulin. An artificial pancreas is a technology that uses a pump to inject the appropriate amount of insulin subcutaneously after analysing information collected by sensors, including continuous blood glucose monitoring. Over the past thirty years, several methods for controlling an artificial pancreas have been investigated in clinical and simulation environments. The improved Hovorka model, a comprehensive nonlinear model that explains the effects of insulin on transport, disposal and endogenous synthesis in both accessible and inaccessible compartments for blood glucose control by insulin administration, is used for this research. The presented model has the characteristics of a switching nonlinear system. The work proposes to analyse different nonlinear control strategies for blood glucose regulation and shows the effectiveness of the linear model predictive control strategy compared to other nonlinear controllers used in the literature.