Iterative Learning Control with Sparse Measurements for Long-Acting Insulin Injections in People with Type 1 Diabetes

People with type 1 diabetes require exogenous insulin for adequate blood glucose regulation. Traditionally, the clinical therapy consists of multiple daily injections (MDIs) of insulin analogs and a finite number of self-monitoring blood glucose measurements (SMBGs) per day to achieve glycemic regulation. In this paper, we present simulation results for once-a-day dosing of long-acting insulin analog using iterative learning control (ILC) to deliver basal insulin. To facilitate validation of the control strategy for MDI, we propose modifications to a metabolic model for type 1 diabetes by adding states related to the subcutaneous insulin kinetics of a generic long-acting insulin. Simulations on the cohort of in-silico patients demonstrate potential of the proposed strategy and its advantages over current clinical practice for basal insulin delivery. In particular, the ILC performs robustly under induced insulin resistance.