Precise Insulin Delivery for Artificial Pancreas: A Reinforcement Learning Optimized Adaptive Fuzzy Control Approach
Journal:
arXiv
Published Date:
Mar 9, 2025
Abstract
This paper explores the application of reinforcement learning to optimize the
parameters of a Type-1 Takagi-Sugeno fuzzy controller, designed to operate as
an artificial pancreas for Type 1 diabetes. The primary challenge in diabetes
management is the dynamic nature of blood glucose levels, which are influenced
by several factors such as meal intake and timing. Traditional controllers
often struggle to adapt to these changes, leading to suboptimal insulin
administration. To address this issue, we employ a reinforcement learning agent
tasked with adjusting 27 parameters of the Takagi-Sugeno fuzzy controller at
each time step, ensuring real-time adaptability. The study's findings
demonstrate that this approach significantly enhances the robustness of the
controller against variations in meal size and timing, while also stabilizing
glucose levels with minimal exogenous insulin. This adaptive method holds
promise for improving the quality of life and health outcomes for individuals
with Type 1 diabetes by providing a more responsive and precise management
tool. Simulation results are given to highlight the effectiveness of the
proposed approach.
