Optimized Control Techniques for Improved Type 1 Diabetes Health Outcomes

Glucose, a simple sugar from dietary carbohydrates, is the primary energy source for cellular functions, including muscle movement and brain activity. The endocrine system reg-ulates glucose levels in the bloodstream to maintain physiological balance and health. Insulin administration, either orally or via an artificial pancreas (AP), utilizes control algorithms based on mathematical models to manage blood glucose levels (BGL). The extended Bergman minimal model (EBMM) is a sophisticated mathematical framework that accounts for fluctuations in insulin levels due to dietary intake or exercise-induced glucose utilization. Our research focuses on implementing Adaptive Backstepping Sliding Mode Control (AB-SMC), a nonlinear control strategy, to stabilize BGL in individuals with type 1 diabetes. This approach aims to manage fluctuations and maintain stability, enhancing health outcomes and quality of life. A key feature of this method is adaptive parameter estimation, which eliminates the need for precise model parameters during the design phase. The optimization of controller gains is achieved using a genetic algorithm, with the integral time absolute error as the objective function. Stability is ensured through Lyapunov control theory, confirming the controller's capacity for asymptotic stability. We assess the effectiveness of the proposed controllers through MAT-LAB/Simulink simulations, complemented by real-time hardware validation using a hardware-in-loop setup with the C2000 Delfino and MCU F28379D Launchpad. This combination of advanced control strategies and robust validation techniques represents a significant step towards more effective management of type 1 diabetes, promising improved patient outcomes and quality of life.