Optimizing fuzzy logic controller for diabetes type I by genetic algorithm

Biological systems are highly nonlinear, and there is a significant amount of variability from one patient to another. A controller for drug infusion has to be able to achieve good performance for most of the potential patient population. These controllers are designed for the general population, as no knowledge is available before hand for each particular patient. This necessitates perforce an algorithm that will be able to compensate for the differences between individuals and external factors. Of the possibilities, fuzzy logic direct model reference adaptive control (DMRAC) is the choice for Insulin infusion control for diabetes type I as results presented herein. Safety and robustness issues must be considered, as a mistake in the infusion of drugs by a controller can be fatal. To this end, we optimize our fuzzy controller with genetic algorithms approach. Second application is the regulation of blood glucose. Modeling is covered in detail, including subcutaneous insulin infusion and glucose measurements, the carbohydrate metabolism, and glucose absorption from meals.