Improved Generalized Predictive Control Algorithm for Blood Glucose Control of Type 1 Diabetes

Artificial pancreas (AP) is an important treatment for patients with Type 1 diabetes (T1D). The control algorithm adopted in an AP system determines its reliability and accuracy. The generalized predictive control (GPC) is a representative adaptive control algorithm and has been widely applied to AP systems. However, we found that the traditional GPC controller does not work well for adolescents with T1D because of their high-fluctuating blood glucose and high insulin resistance. Here, we propose an improved GPC algorithm with an adaptive reference glucose trajectory and an adaptive softening factor. The slopes of the reference trajectory and the value of softening factor are calculated real-time on the basis of the blood glucose concentration (BGC) variations. In silico testing was done using the US Food and Drug Administration (FDA) approved virtual patient software T1D mellitus. The BGC trace and density of 20 patient-subjects (10 adults and 10 adolescents) were recorded. Results showed that the average BGC percentage within the target regions (70-180 mg/dL) of the tests with adaptive reference glucose trajectory and softening factor for adolescents (0.93 ± 0.07) was significantly higher than that of the traditional GPC algorithm tests (0.88 ± 0.11), suggesting that the control quality of the blood glucose of adolescents is significantly improved with our GPC algorithm. Therefore, our improved GPC controller is effective and should have a good applicability in AP systems.