A Systematic Review of Fuzzy Logic Controllers for Blood Glucose-Insulin Systems: Design, Performance, and Integration into Artificial Pancreas

The regulation of blood glucose levels in diabetic patients remains a significant biomedical control challenge due to nonlinear metabolic dynamics, time delays, sensor noise, and uncertain disturbances such as meals and exercise. Traditional controllers, such as Proportional-Integral-Derivative (PID) and Model Predictive Control (MPC), often fail to provide robust performance under these uncertainties. Fuzzy Logic Controllers (FLCs), inspired by human reasoning, offer adaptive and robust control in nonlinear, uncertain environments. This review comprehensively surveys the design, development, and application of fuzzy controllers in blood glucose-insulin systems (BGIS). We discuss mathematical modeling approaches, controller structures, comparative analyses with conventional methods, and recent advancements including adaptive, type-۲, and neuro-fuzzy systems. Furthermore, real-world implementations such as hybrid closed-loop artificial pancreas systems (e.g., Omnipod ۵, Medtronic ۷۸۰G) are reviewed. Challenges and future perspectives are presented to guide future research toward fully autonomous and intelligent diabetes management systems.

کلیدواژه ها:

Fuzzy logic ، Blood glucose regulation ، Insulin pump ، Artificial pancreas ، Closed-loop control ، Diabetes