Modeling, Identification, and Control towards Adaptive Personalized Glucose Management for Insulin-Deficient Diabetes

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The research objective of this award is to develop modeling and control algorithms for personalized adaptive insulin therapy. The algorithms can be used in the artificial pancreas for type-1 diabetes, but also would be useful for glucose management for insulin-dependent type-2 diabetes. This research will characterize patient-specific glucose dynamics by data-driven models with time-varying parameters that can be adjusted for the effects of food intake, physical activity and emotional tone under natural living conditions. This research will also develop adaptive control algorithms that enable a better control of hyperglycemia and prevention of hypoglycemia. Robustness issues will be addressed with respect to uncertainties in estimation of carb content, uncertain time-delay of meal intake, and variations in insulin and carb sensitivities. Deliverables include easy-to-implement real-time parameter estimation algorithms for insulin-glucose kinetics, adaptive control algorithms for insulin delivery, evaluation and validation via a FDA-approved simulator for type-1 diabetes, documentation of research results, and engineering student education.If successful, the results of this research will facilitate the development of a mechanical artificial pancreas by means of providing a control algorithm used to close the loop between glucose sensing and insulin delivery in the artificial pancreas. Patients with type-1 or type-2 diabetes will benefit from the results of this research by lowering the risk of many of the devastating long-term complications. After testing and evaluation through FDA-approved simulation platforms, the results are transformable to be used in insulin pumps for clinical tests. Engineering students will get involved in this research through senior design projects as well as projects in undergraduate- and graduate-level control courses, e.g., design of user-friendly tools in aiding the control of diet and improvement of control algorithms.