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Adaptive fuzzy decentralized output feedback control for stochastic nonlinear large‐scale systems using DSC technique
Author(s) -
Tong Shaocheng,
Li Yongming,
Wang Tao
Publication year - 2011
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.1834
Subject(s) - backstepping , control theory (sociology) , fuzzy logic , nonlinear system , observer (physics) , fuzzy control system , state observer , computer science , bounded function , uniform boundedness , adaptive control , mathematics , mathematical optimization , control (management) , artificial intelligence , mathematical analysis , physics , quantum mechanics
SUMMARY In this paper, an adaptive fuzzy decentralized backstepping output feedback control approach is proposed for a class of uncertain large‐scale stochastic nonlinear systems 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. Using the designed fuzzy state observer, and by combining the adaptive backstepping technique with dynamic surface control technique, an adaptive fuzzy decentralized output feedback control approach is developed. It is shown that the proposed control approach can guarantee that all the signals of the resulting closed‐loop system are semi‐globally uniformly ultimately bounded in probability, and the observer errors and the output of the system converge to a small neighborhood of the origin by choosing appropriate design parameters. A simulation example is provided to show the effectiveness of the proposed approaches. Copyright © 2011 John Wiley & Sons, Ltd.