Design of reduced multiple model predictive controller for glucose–insulin regulation in type 1 diabetic patients

Abstract

To maintain stable glucose level, diabetic patients should monitor and control their glucose levels several times a day through insulin injections. However, it is important to acknowledge that errors can occur during such a process, which may affect the desired outcomes. The artificial pancreas is designed to overcome such undesirable situations using a closed-loop control approach. The vital impressing challenge to closed-loop control is dealing with the complexity of glucose–insulin model, including model order and nonlinearity. This paper presents a closed-loop reduced multiple model predictive control to address these complexities, aiming to ease the monitoring and controlling glucose level in type 1 diabetic patients. Considering the influence of meal patterns on glucose control, the reduced model predictive control system is capable of effectively maintaining a safe and stable glucose level. Additionally, cost function in model predictive control could be defined to optimize both the insulin dosage and the glucose level, ensuring a well-balanced approach between the two. To assess the effectiveness of the proposed method, simulations using the Hovorka nonlinear glucose–insulin system are conducted in this regard.