In this paper, Blood Glucose control in type 1 diabetic patients in the presence of structured and unstructured uncertainties is studied. In order to increase the effectiveness of the proposed control approach, assumed that all the dynamics describing the regulation of
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In this paper, Blood Glucose control in type 1 diabetic patients in the presence of structured and unstructured uncertainties is studied. In order to increase the effectiveness of the proposed control approach, assumed that all the dynamics describing the regulation of Blood Glucose in type 1 diabetic patients are completely unknown. Based on the fuzzy approximation function, which is equipped with the adaptive algorithm and employing the approach of reducing the number of adaptive fuzzy parameters, the unknown dynamics of the Bergman model approximated. Then, based on the feedback linearization control approach and robust adaptive compensator, the design of feedback linearization robust fuzzy controller to regulate Blood Glucose in type 1 diabetic patients in the presence of meal is studied for the first time. Using Lyapunov theory, it is shown that all signals of the closed-loop system are uniformly ultimately bounded and the Blood Glucose of diabetic patients converges to the neighborhood of the desired value. Finally, the simulation results show a good controller performance in reducing effect of the meal disturbance, and robustness against uncertain dynamics and meal estimation errors. Moreover, in comparison with some existing results, a good performance of the introduced controller in controlling Blood Glucose of diabetic patients (i.e., keeping Blood Glucose in allowed range 70-120 mg/dl) validated.
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