  Hepatitis type B is one of the most common infectious disease worldwide that
can pose severe threats to human health up to the point that may contribute to
severe liver damage or cancer. Over the past two decades a large number of
dynamic models have been presented based on experimental data to predict the
HBV infection behavior. Besides, several kinds of controllers have been
employed to obtain effective solutions from the HBV treatment. In this essay we
consider the nonlinear HBV dynamic model which subjected to both parametric and
non-parametric uncertainties without using any linearization. In previous
control methods three HBV dynamic states should be measured virus safe, and
infected cells. However in most of the biological systems, the amount of virus
is experimentally measured. Accordingly, the necessity to seek a method that
can estimate the amount of required drug by receiving the virus data emerges.
An ANFIS method is developed in this work to provide an intelligent controller
for the drug dosage based on the number of viruses together with an estimator
for the amount of infected and uninfected cells. This controller is trained
first using the data provided from a previous adaptive control strategy. After
that to improve the closed-loop system capabilities two unmeasured state
variables of fundamental dynamics are estimated through the training phase of
the ANFIS observer. The results of simulations demonstrated that the accuracy
of the proposed intelligent controller is high in the tracking of the desired
descending virus population.
