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In this paper, adaptive neural control (ANC) is investigated for a class ofstrict-feedback nonlinear stochastic systems with unknown parameters, unknownnonlinear functions and stochastic disturbances. The new controller of adaptiveneural network with state feedback is presented by using a universalapproximation of radial basis function neural network and backstepping. Anadaptive neural network state-feedback controller is designed by constructing asuitable Lyapunov function. Adaptive bounding design technique is used to dealwith the unknown nonlinear functions and unknown parameters. It is shown that,the global asymptotically stable in probability can be achieved for theclosed-loop system. The simulation results are presented to demonstrate theeffectiveness of the proposed control strategy in the presence of unknownparameters, unknown nonlinear functions and stochastic disturbances.

Adaptive Neural Control for a Class of Stochastic Nonlinear Systems with Unknown Parameters, Unknown Nonlinear Functions and Stochastic Disturbances