Event-Triggered Model Predictive Control for Artificial Pancreas Using Prior-Knowledge-Informed Hybrid Model

The artificial pancreas (AP) systems based on model predictive control (MPC) are expected to provide effective and safe regulation of blood glucose concentration (BGC) for people with type 1 diabetes. However, the development of AP systems based on MPC is challenged by the high computational burden of MPC facing limited computational resources and energy supply of portable devices. In this article, we proposed a novel event-triggered MPC (ET-MPC) to reduce the computational burden and lower the energy consumption of fully automated AP systems. The predictor in the ET-MPC is based on a hybrid glucose prediction model that incorporates prior knowledge of glucose dynamics. By exploring the distribution of modeling residuals, it enables the model to capture changes in glucose concentration dynamics and avoid unnecessary updates of model parameters. Event-triggered (ET) rules aim to lower the energy consumption of the optimizer, realized by reducing the times of solving optimization problems. They are proposed by leveraging the physiological insights of glucose dynamics and fundamental mechanisms of MPC. The efficiency and advantages of the proposed ET-MPC are illustrated through preclinical simulation studies challenged by unannounced meals and physical activity. Overall, the predictor and optimizer are updated for 2.4% and 28.8%, respectively, of time over the closed-loop experiment period with the appeal that BGC is maintained within the safe range for 79.7% of time, which is not significantly different from the results when ET rules are not utilized. The proposed ET-MPC enables the AP system to manage BGC effectively with a considerably low computational burden and energy consumption.