Adaptive robust tracking control algorithm and on-line learning for the glucoregulatory system

This paper introduces an intelligent system developed for the online learning and adaptive control of the human glucose metabolism. The measurement process of the glucose concentration usually involves significant time-delay. The metabolic system was modelled by a 4^th order digital model with time-delay, and a three term microcontroller is employed in real-time processing of such system. The adaptive control of this system was achieved using a recently developed criterion such that the closed-loop dynamics closely track a described and well-behaved trajectory. The instrumental variable estimator (IV) was used for online learning of such system by successively adjusting the model parameters based on a set of input-output patterns incorporating an output disturbance of stochastic nature. The design and implementation of this intelligent system is presented by simulating a realistic system using a LabTop computer.