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Observer‐based adaptive neural control for a class of nonlinear singular systems
Author(s) -
Chen Jian,
Lin Chong,
Chen Bing,
Zhang Ziye
Publication year - 2020
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.4980
Subject(s) - backstepping , control theory (sociology) , nonlinear system , impulse (physics) , observer (physics) , artificial neural network , state observer , algebraic number , mathematics , adaptive control , controller (irrigation) , bounded function , computer science , strict feedback form , control (management) , artificial intelligence , mathematical analysis , physics , quantum mechanics , agronomy , biology
Summary The problem of observer‐based adaptive neural control via output feedback for a class of uncertain nonlinear singular systems is studied in this article. The nonlinear singular systems can be regarded as two subsystems that are coupled with each other: differential subsystem and algebraic subsystem. The differential systems can be nonstrict feedback structures. To guarantee that the singular system is regular and impulse‐free, two new conditions are proposed. By the conditions, the linear controller and observer, which are used to estimate the immeasurable state variables, are obtained. Then, an output feedback scheme through adaptive neural backstepping is proposed to ensure that all states of the closed‐loop system are semiglobally uniformly ultimately bounded and converge to a small neighborhood of the origin. Simulation examples illustrate the effectiveness of the presented method.