A Reinforcement Learning Tuned Hybrid Type-3 Fuzzy Logic Controller for Exercise and Meal Aware Blood Glucose Regulation in Type 1 Diabetics for Artificial Pancreas

Abstract

Effective blood glucose regulation in individuals with Type 1 diabetes remains a challenging control problem due to strong nonlinearities, inter-patient variability, and unmodeled disturbances such as physical activity. Exercise introduces rapid and highly uncertain changes in insulin sensitivity, often leading to hypoglycemic events when inadequately handled. To address these challenges, this paper proposes an exercise-aware artificial pancreas framework based on a Reinforcement Learning (RL) tuned Hybrid Type-3 fuzzy logic controller. The proposed controller explicitly models higher-order uncertainty through Type-3 fuzzy membership functions, enabling improved representation of intra and inter-day physiological variability compared to Type-1 and Type-2 fuzzy systems. An established exercise model is adopted to design and test the controller. Monte Carlo simulations are performed to demonstrate robustness under wide-ranging metabolic uncertainties and physical activity conditions. Simulation results demonstrate that the proposed RL-tuned Hybrid Type-3 fuzzy controller effectively converges glucose levels to the desired value and significantly improves glucose regulation performance, reducing both hyperglycemic and hypoglycemic events while maintaining stable insulin delivery during post-meal and exercise periods, exhibiting enhanced robustness, adaptability, and safety. These results suggest that combining exercise modeling with reinforcement learning optimized Type-3 fuzzy control offers a promising pathway toward reliable and patient-resilient artificial pancreas systems.