Intelligent cost-effective bio-medical type-3 fuzzy model and controller

In this study, the problem of glucose regulation for diabetic patients is studied and a new artificial pancreas is developed. In this paper, a new fractional-order dynamic type-3 (T3) fuzzy logic model (FLM) is designed for adaptively glucose–insulin metabolism identification. Based on the identified metabolism, a predictive T3 fuzzy logic controller (T3-FLC) is designed. The designed T3-FLC is learned using a reinforcement learning scheme based on the method of Eligibility Traces (ET) algorithm. ET is an algorithm between Monte Carlo and Temporary Differences Learning. In ET to update the pair of states and agents, the whole chain of states and actions before are considered. The predictor–corrector method of Adams–Bashforth is used to predict the estimated glucose level (output of fractional-order T3-FLM) for a prediction horizon. New Lyapunov adaptations are derived such that the dynamics of the glucose regulation error are stable. Furthermore, a new adaptive parallel supplementary controller is designed to consider the medical restrictions (upper bounds of injected insulin, limitation of sensors and actuators). The comprehensive analyses are provided to show the feasibility and good efficiency of the designed controller.