Robust adaptive fuzzy control for a class of stochastic nonlinear systems with dynamical uncertainties

In this paper, a robust adaptive fuzzy output feedback control approach is developed for a class of uncertain stochastic nonlinear systems with unknown nonlinear functions, dynamical uncertainties and without the measurements of the states. The fuzzy logic systems are used to approximate the unknown nonlinear functions, and a fuzzy state observer is designed for estimating the unmeasured states. To solve the problem of the dynamical uncertainties, the dynamical signal combined with changing supply function is incorporated into the backstepping recursive design technique, and a new robust adaptive fuzzy output feedback control scheme is constructed. It is proved that all the solutions of the closed-loop system are bounded in probability, and the observer errors and the output of the system converge to a small neighborhood of the origin by choosing design parameters appropriately. Two simulation examples are provided to demonstrate the effectiveness of the proposed control approach. (C) 2012 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.