Parameter optimisation of Artificial Pancreas adaptive controller using genetic algorithm

We propose an optimisation method for parameter setting of a MRAC-based adaptive controller of blood glucose (glycemia) in the human body. The glycemia dynamics is a complex process, which can be controlled by external bio-cybernetic devices as Artificial Pancreas. The artificial pancreas consists of glycemia sensor, controller and insulin pump. Such systems are using complex algorithms with many parameters which have to be set by the designer and which have significant influence on the quality of glycemia control. Since conventional parameter design methods do not give satisfactory results a simulation-based optimisation method which is based on genetic algorithms was proposed. The proposed method gives good results and it can be used also for design of other complex bio-cybernetic systems.