Model Predictive Control of Glucose Concentration Based on Signal Temporal Logic Specifications with Unknown-Meals Occurrence

Abstract

The glycemia regulation is a significant challenge in the Artificial Pancreas (AP) scenario. Several control systems have been developed in the last years, many of them requiring meal announcements. Therefore, if the patients skip the meal announcement or make a mistake in the estimation of the amount of carbohydrates, the control performance will be negatively affected. In this extended version of our previous work, we present a Model Predictive Controller (MPC) for the AP in which the meal is treated as a disturbance to be estimated by an Unknown Input Observer (UIO). The MPC constraints are expressed in terms of Signal Temporal Logic (STL) specifications. Indeed, in the AP some requirements result in hard constraints (in particular, absolutely avoid hypoglycemia and absolutely avoid severe hyperglycemia) and some other in soft constraints (avoid a prolonged hyperglycemia) and STL is suitable for expressing such requirements. The achieved results are obtained using the BluSTL toolbox, which allows to synthesize model predictive controllers with STL constraints. We report simulations showing that the proposed approach, avoiding unnecessary restrictions, provides safe trajectories in correspondence of higher unknown disturbance.