Adaptive tracking control for a class of stochastic nonlinear systems

Using the technique of neural network parameterization and the backstepping method, a novel adaptive neural network control scheme is proposed for a class of stochastic strict-feedback nonlinear systems. Compared with the existing literature, the proposed approach contains only one adaptive parameter that needs to be updated online. By Lyapunov method, it is shown that all signals in the closed-loop system are semi-globally uniformly ultimately bounded in mean square or the sense of four-moment. Simulation results are given to illustrate the effectiveness of the proposed control scheme.