Insulin Sensitivity Management in Artificial Pancreas: a Switching Control Strategy Approach – An In Silico Study

Management of insulin sensitivity variability poses a significant challenge in achieving optimal blood glucose control for Type 1 Diabetes Mellitus (T1DM) patients using Artificial Pancreas (AP) systems. Traditional control strategies, particularly those employing Linear Time-Invariant (LTI) models in Model Predictive Control (MPC), although effective, do not adequately address the pronounced circadian fluctuations in insulin sensitivity. This study proposes an innovative switching MPC strategy leveraging multiple linear models, each corresponding to distinct daily periods (i.e., morning, afternoon, and evening) to dynamically adapt insulin dosing. The flexibility of the switching algorithm allows transitions between models within predefined, physiologically appropriate time windows. Performance evaluation, conducted via simulations using the UVA/Padova T1DM simulator, demonstrates that the proposed switching control strategy substantially reduces hypoglycemic episodes and stabilizes glucose variability compared to traditional single-model MPC approaches. This adaptive method-ology shows promise in enhancing the safety and efficacy of glucose management, paving the way for improved quality of life and reduced diabetes-related complications.