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Adaptive prescribed performance decentralized control for stochastic nonlinear large‐scale systems
Author(s) -
Shao Xinfeng,
Tong Shaocheng
Publication year - 2018
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.2944
Subject(s) - backstepping , control theory (sociology) , fuzzy logic , nonlinear system , lyapunov stability , observer (physics) , fuzzy control system , adaptive control , tracking error , decentralised system , computer science , lyapunov function , stability (learning theory) , bounded function , mathematical optimization , mathematics , control (management) , artificial intelligence , mathematical analysis , physics , quantum mechanics , machine learning
Summary This paper investigates the adaptive fuzzy prescribed performance output‐feedback decentralized control problem for a class of stochastic interconnected nonlinear large‐scale systems. The fuzzy logic systems are used to approximate the unknown nonlinear functions, and a state observer is designed to estimate the unmeasured states. By combining the backstepping recursive design principle with prescribed performance theory, an adaptive fuzzy decentralized control method is presented. In order to overcome the problem of “explosion of complexity” in the adaptive backstepping control design, the first‐order filter signals are introduced into adaptive fuzzy decentralized control design algorithm and form a new simplized. The stability is proven based on the Lyapunov stability theory, which demonstrated that all the signals of the closed‐loop system are semiglobally uniformly ultimately bounded in probability and the tracking errors remain a small neighborhood of the origin within the prescribed performance bounds. Finally, a simulation example is provided to illustrate the effectiveness of the proposed approach.