Decentralized adaptive fuzzy output feedback control of stochastic nonlinear large-scale systems with dynamic uncertainties

In this paper, centralized and decentralized adaptive fuzzy output feedback control schemes are investigated for a class of stochastic nonlinear interconnected large-scale systems with dynamic uncertainties and unmeasured states. Fuzzy systems are used to approximate the unknown nonlinear functions. Decentralized K-filters are designed to estimate the unmeasured states. An available dynamic signal is introduced to dominate the unmodeled dynamics. By combining dynamic surface control (DSC) technique with backstepping design, the condition in which the approximation errors are assumed to be bounded is avoided. Using the defined compact set in the stability analysis, the unknown smooth interconnections and black box functions are effectively dealt with. Using Itoˆ formula and Chebyshev’s inequality, it is shown that all the signals in the closed-loop system are bounded in probability, and the error signals are semi-globally uniformly ultimately bounded in mean square or the sense of four-moment. Simulation results demonstrate the effectiveness of the proposed approach.