Safe Data-Driven Optimal control for type-1 Diabetes

This work introduces a safe data-driven control methodology, Data-Enabled Predictive Control (DeePC), for the control of blood glucose in type-1 diabetic patients. DeePC utilizes input-output trajectory data directly without requiring a system model or state estimation like other modelbased algorithms. The control strategy is validated using the Bergman Minimal Model, a well-established framework for glucose-insulin dynamics. Comparative simulations are conducted against Proportional-Integral-Derivative (PID) and Model Predictive Control (MPC) strategies. Results show that DeePC achieves comparable or superior glycemic regulation, particularly under model uncertainty, by maintaining normoglycemia and reducing hypoglycemia risk. The findings demonstrate the robustness and potential of DeePC in biomedical applications where model accuracy is uncertain. Future works would include computational efficiency improvements and handling uncertainties in meal estimation.