Abstract:

The problem of adaptive stabilization is addressed for a class of uncertain stochastic nonlinear strict-feedback systems with both unknown dead-zone and unknown gain functions. By using the backstepping method and neural network (NN) parameterization, a novel adaptive neural control scheme which contains fewer learning parameters is developed to solve the stabilization problem of such systems. Meanwhile, stability analysis is presented to guarantee that all the error variables are semi-globally uniformly ultimately bounded with desired probability in a compact set. The effectiveness of the proposed design is illustrated by simulation results.