  In this paper, adaptive neural control (ANC) is investigated for a class of
strict-feedback nonlinear stochastic systems with unknown parameters, unknown
nonlinear functions and stochastic disturbances. The new controller of adaptive
neural network with state feedback is presented by using a universal
approximation of radial basis function neural network and backstepping. An
adaptive neural network state-feedback controller is designed by constructing a
suitable Lyapunov function. Adaptive bounding design technique is used to deal
with the unknown nonlinear functions and unknown parameters. It is shown that,
the global asymptotically stable in probability can be achieved for the
closed-loop system. The simulation results are presented to demonstrate the
effectiveness of the proposed control strategy in the presence of unknown
parameters, unknown nonlinear functions and stochastic disturbances.
