Multiple model adaptive postprandial glucose control of type 1 diabetes

Abstract

In this work, the adaptive regulation of blood glucose (BG) in type I diabetic (T1D) patients is considered by developing a Multiple Model Adaptive Control (MMAC), where its estimation is based on Magdelaine's long-term glucose-insulin Model. The (MMAC) is built using a bank of Kalman- Bucy Filters (KBFs)with optimal state feedback controllers. Each KBF is based on a particular value of the equilibrium point for which, the optimal Linear Quadratic Servo (LQ-Servo) controller is designed. The total state estimation is resolved by the probabilistic weighted sum of the produced outputs of all filters based on measured glucose signal. Simulation results show that MMAC is capable of providing reliable estimation and regulation of insulin delivery. Moreover, the performance of the controlled glucose/insulin is improved by 99% compared with that when using a single KBF. The MMAC has accurately identified the glucose signal corresponding to the hypothesis models with an average accuracy of 96.4% for 5 tested patients. Robust performance has been tested with different initial conditions and disturbance.

Keywords

• Artificial pancreas
• Diabetes control
• Optimal control
• Multiple Model Adaptive Control (MMAC)
• Kalman-Bucy Filter (KBF)
• Biomedical engineering