Stochastic Model Predictive Control of Blood Glucose Levels using Probabilistic Meal Anticipation

Unannounced meals introduce substantial disturbances, causing large deviations in blood glucose concentrations from the desired range. Accurate estimation of meal timing and size is crucial for precise state estimation in a Kalman filter. Achieving accurate meal estimation remains a challenging task for fully-automated insulin delivery systems. This paper proposes incorporating a correction mechanism for the estimated states, where missed meals are detected by a neural network. Additionally, a Bayesian network is utilized to forecast timing probabilities of the next meal. Our proposed stochastic model predictive controller (SMPC) incorporates predicted meal scenarios. We evaluate the controller performance with respect to the stochasticity of the dietary patterns; the results illustrate that integrating the most likely meal scenarios into SMPC decision-making enhances both robustness and performance.