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Adaptive output feedback stabilization for a class of nonlinear systems with inherent nonlinearities and uncertainties
Author(s) -
Shang Fang,
Liu Yungang,
Zhang Chenghui
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.1583
Subject(s) - backstepping , control theory (sociology) , nonlinear system , correctness , observer (physics) , stability (learning theory) , class (philosophy) , constant (computer programming) , computer science , output feedback , adaptive control , scheme (mathematics) , control (management) , mathematics , algorithm , artificial intelligence , mathematical analysis , physics , quantum mechanics , machine learning , programming language
This paper investigates the problem of adaptive stabilization by output feedback for a class of uncertain nonlinear systems. The distinguishing feature of such a class of systems is the presence of uncertain control coefficient and unmeasured states dependent growth with growth rate of polynomial‐of‐output multiplying an unknown constant. First, new high‐gain K‐filters with two dynamic gains are introduced, and an appropriate state observer is constructed based on the K‐filters. Then, motivated by the universal control idea, the backstepping scheme is successfully developed for the adaptive output feedback control design. By appropriate choice of the design parameters, the global stability of the closed‐loop system can be guaranteed. Finally, numerical simulations are provided to illustrate the correctness of the theoretical results. Copyright © 2010 John Wiley & Sons, Ltd.