Adaptive Fuzzy Decentralized Output Stabilization for Stochastic Nonlinear Large-Scale Systems With Unknown Control Directions

Abstract

In this paper, an adaptive decentralized fuzzy output feedback stabilization problem is investigated for a class of uncertain stochastic nonlinear large-scale systems. The addressed stochastic nonlinear systems contain unknown nonlinear functions, unknown control direction, and without the measurements of the states. Fuzzy logic systems are used to identify the unknown nonlinear functions, and a fuzzy state filter observer is designed to estimate the unmeasured states. To solve the problem of the unknown control direction in decentralized control design, Nussbaum-type functions are introduced and new property on Nussbaum-type function is proved. Based on the backstepping recursive design technique and the established Nussbaum function property, a new robust stabilization control approach is developed. It is proved that the proposed control approach can guarantee that all the signals of the resulting closed-loop system are bounded in probability, and the observer errors and system output converge to a small neighborhood of the origin. A simulation example is provided to show the effectiveness of the proposed approach.