Adaptive neural control for a class of stochastic nonlinear pure-feedback systems with unknown control direction

This paper addresses a class of uncertain stochastic nonlinear pure-feedback systems with unknown control direction. With using the decoupled backstepping technique, adaptive neural control schemes are designed to solve the stabilization problem of such systems. Stability analysis is presented to guarantee that all the error variables are semi-globally ultimately bounded with desired probability in a compact set. The effectiveness of the proposed design is verified by simulation results.