Using a fuzzy controller optimized by a genetic algorithm to regulate blood glucose level in type 1 diabetes

In this paper a closed-loop control algorithm for blood glucose regulation in type 1 diabetic patients is proposed by using the Mamdani-type fuzzy method. Because of the presence of high-pass proportional derivatives in fuzzy designing, optimal values are applied for two inputs and one output membership functions in order to prevent the fluctuations due to derivatives in fuzzy design. Therefore, 19 values which are related to membership functions of the two inputs and one output are obtained by using a genetic algorithm (GA). The new model, termed the Augmented Minimal Model (AMM), is used in simulations. This controller is capable of stabilizing the blood glucose concentration at a normoglycaemic level of 90 mg dl−1. The operation of the controller under various situations including multiple meal disturbances, and noise due to inaccurate effects of measuring blood glucose level are considered. Uncertainties in the meal disturbance function and variations of model parameters were also taken into consideration in simulations and the controller was found to be robust to such uncertainties.

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