In this paper, we propose and design an adaptive dual controller for automatic glucose control of diabetic patients. The results could be used in the development of an artificial pancreas, which, while as yet unavailable, must consist of three major components: an insulin delivery device or “pump”, a continuous glucose sensor, and a control algorithm linking insulin delivery to measured glucose concentration. For improved performance the system would also include “feed-forward” information about food intake, physical activity and other blood glucose perturbing inputs. A linear time-varying autoregressive model with exogenous inputs is constructed to characterize the kinetics of both glucose-insulin and glucose-carbohydrate interaction. Combined with a Kalman-filter based estimation scheme for online estimation of the time-varying model coefficients, we design an adaptive dual control that both excites the glucose dynamic system sufficiently to accelerate the parameter estimation and cautiously tracks the desired glucose level. Performance evaluation of the adaptive dual controller is accomplished via simulations on virtual patients constructed using clinical data from five different patients with type-1 insulin-deficient diabetes using continuous subcutaneous insulin infusion for diabetes management during observation. Simulation results show both smaller glucose excursions and a reduction in the number of hypoglycemic events for all but one of the five subjects.
- Dynamic Systems and Control Division
Development of an Adaptive Dual Controller for Glucose Management in Patients With Insulin-Deficient Diabetes
Freeman, KA, Wang, Q, Molenaar, P, Ulbrecht, J, Gold, CH, & Rovine, M. "Development of an Adaptive Dual Controller for Glucose Management in Patients With Insulin-Deficient Diabetes." Proceedings of the ASME 2012 5th Annual Dynamic Systems and Control Conference joint with the JSME 2012 11th Motion and Vibration Conference. Volume 1: Adaptive Control; Advanced Vehicle Propulsion Systems; Aerospace Systems; Autonomous Systems; Battery Modeling; Biochemical Systems; Control Over Networks; Control Systems Design; Cooperative and Decentralized Control; Dynamic System Modeling; Dynamical Modeling and Diagnostics in Biomedical Systems; Dynamics and Control in Medicine and Biology; Estimation and Fault Detection; Estimation and Fault Detection for Vehicle Applications; Fluid Power Systems; Human Assistive Systems and Wearable Robots; Human-in-the-Loop Systems; Intelligent Transportation Systems; Learning Control. Fort Lauderdale, Florida, USA. October 17–19, 2012. pp. 345-354. ASME. https://doi.org/10.1115/DSCC2012-MOVIC2012-8803
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