  This paper deals with the tracking control problem for a very simple class of
unknown nonlinear systems. In this paper, we presents a design strategy for
tracking control of time-varying state constrained nonlinear systems in an
adaptive framework. The controller is designed using the backstepping method.
While designing it, Barrier Lyapunov Function (BLF) is used so that the state
variables do not contravene its constraints. In order to cope with the unknown
dynamics of the system, an online approximator is designed using a neural
network with a novel adaptive law for its weight update. To make the controller
robust and computationally inexpensive, a disturbance observer is proposed to
cope with the disturbance along with neural network approximation error and the
time derivative of virtual control input. The effectiveness of the proposed
approach is demonstrated through a simulation study.
