In silico testing of optimized Fuzzy P+D controller for artificial pancreas

Abstract

Background and objectivesDespite therapeutic advances, a complete cure has not been found yet for patients with type 1 diabetes (T1D). Artificial pancreas (AP) is a promising approach to cope with this disease. The controller part of the AP can compute the insulin infusion rate that keeps blood glucose concentration (BGC) in normoglycemic ranges. Most controllers rely on model-based controllers and use manual meal announcements or meal detection algorithms. For a fully automated AP, a controller only using the patient's BGC data is needed. MethodsAn optimized Mamdani-type hybrid Fuzzy P+D controller was proposed. Using the University of Virginia/Padova Simulator, a 36h scenario was tested in nine virtual adult patients. To take into account the effect of continuous glucose monitor noise, the scenario was repeated 25 times for each adult. The main outcomes were the percentage of time BGC levels in the euglycemic range, low blood glucose index (LBGI), and blood glucose risk index (BGRI), respectively. ResultsThe obtained BGC values were found to be in the euglycemic range for 82.6% of the time. Moreover, the BGC values were below 50mg/dl, below 70mg/dl and above 250mg/dl for 0%, 0.35% and 0.74% of the time, respectively. The BGRI, LBGI, and high blood glucose index (HBGI) were also found as 3.75, 0.34 and 3.41, respectively. The proposed controller both increases the time the BGC levels in the euglycemic range and causes less hypoglycemia and hyperglycemia relative to the published techniques studied in a similar scenario and population.