Use of a “Fuzzy Logic” Controller in a Closed-Loop Artificial Pancreas

BACKGROUND: Most current model-based approaches to closed-loop artificial pancreas systems rely on mathematical equations describing the human glucoregulatory system; however, incorporating the various physiological parameters (e.g., illness, stress) into these models has been problematic. We evaluated a fully automated "fuzzy logic" (FL) closed-loop insulin dosing controller that does not require differential equations of the glucoregulatory system and allows clinicians to personalize dosing aggressiveness to meet individual patient requirements. SUBJECTS AND METHODS: This pilot study evaluated the FL controller in the setting of bed rest in a very controlled environment. Two carbohydrate-controlled meals were given (30 g at 8 a.m. and 60 g at 2 p.m. without meal announcement or premeal bolus. The primary end point of the study was avoidance of hypoglycemia, defined at <60 mg/dL. Multiple end points related to the frequency and severity of hyperglycemia and hypoglycemia were also assessed. RESULTS: Of the 12 subjects we recruited, 10 were enrolled, and seven completed the study. Two of the enrolled subjects were discontinued because of hypoglycemia; the other was discontinued because of sensor failure. Seven of the 10 subjects who completed the study had average blood glucose values of 165 mg/dL and were within a specified target blood glucose range (70-200 mg/dL) for 76% of the 24-h study period. CONCLUSIONS: Our findings suggest that the FL controller provides a viable alternative to model-based controllers as a component of a closed-loop insulin delivery system. Furthermore, our FL controller allows clinicians to easily specify the level of glucose control based on each patient's clinical needs.