Mixed Linear-Quadratic Cost Function Design for MPC of an Artificial Pancreas - Improved Treatment & Safety for a Broad Range of Meal Sizes

Abstract

A novel Model Predictive Control (MPC) strategy for an artificial pancreas to treat people with type 1 diabetes mellitus is proposed and tested in-silico. The proposed controller has been designed to handle a large spectrum of meal sizes more effectively than the authors’ current strategy Hyperglycemia due to small meals is treated more assertively leading to increased time in euglycemia, whereas hyperglycemia due to large meals is treated more cautiously resulting in a markedly reduced risk of controller-induced hypoglycemia. This is achieved by replacing the purely quadratic cost function employed within the MPC optimization by a mixed linear-quadratic cost function. This simple modification results in significant leeway to design a controller that responds more effectively and safely to the dietary habits of the AP user, while maintaining the quadratic program structure of the resulting optimization problem. The proposed MPC strategy’s benefits are demonstrated by in-silico analysis using the Universities of Virginia/Padova US Food & Drug Administration accepted metabolic simulator.