Extremism Seeking Control Based Zone Adaptation for Zone Model Predictive Control in Type 1 Diabetes

Clinical trials have demonstrated that zone model predictive control is an effective closed-loop blood glucose regulation method for people with type 1 diabetes (T1D). This paper presents a universal model-free optimization method to seek an optimal zone for T1D patients individually. A clinical glycemic risk index named relative regularized glycemic penalty index (rrGPI) is used as the cost function. The proposed method is based on extremum seeking control that uses only the rrGPI index, calculated from measurements by a continuous glucose monitor, to update a controller's blood glucose target zone's upper bound and lower bound simultaneously. The method proposed uses a decaying feedback gain and a vanishing dither signal to improve the extremum seeking controller's robustness against various uncertainties. In silico trials suggest that the proposed method is able to converge to the personalized optimal zone in less than a week of adaptation. In a 30-day in silico trial, the time spent in the range [70,180] mg/dL is increased by about 3% and 2% for unannounced 60 gCHO (grams of carbohydrates) and 90 gCHO meals, respectively, compared to the zone [80,140] mg/dL employed in the authors' current zone controller. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.