Adaptive output feedback dynamic surface control for a class of stochastic nonlinear systems

In this paper, an adaptive output-feedback control scheme is presented for a class of stochastic nonlinear systems with dynamic uncertainties and unmeasured states. Radial basis function neural networks are used to approximate the unknown nonlinear functions. K-filters are designed to estimate the unmeasured states. The changing supply function is employed to solve the problem of dynamical uncertainties. By combining dynamic surface control technique with output-feedback control, the explosion of complexity in traditional backstepping design is avoided. The designed adaptive dynamic surface controller can guarantee all the signals in the closed-loop system are semi-globally uniformly ultimately bounded in probability, and the output of the system converges to a small neighborhood of the origin.