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Nonidentifier‐based adaptive control for nonlinearly parameterized systems with measurement uncertainty
Author(s) -
Zhang Xu,
Lin Wei
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.4921
Subject(s) - control theory (sociology) , parameterized complexity , parametric statistics , nonlinear system , observer (physics) , estimator , controller (irrigation) , constant (computer programming) , mathematics , output feedback , adaptive control , linear system , nonlinear control , polynomial , computer science , control (management) , algorithm , mathematical analysis , statistics , physics , quantum mechanics , artificial intelligence , agronomy , biology , programming language
Summary For a family of nonlinear systems with parametric uncertainty in both state and output equations, we prove that global adaptive regulation is still achievable by output feedback. The bounds of the time‐varying parameter at the system output are unknown, and the class of nonlinear systems is assumed to be dominated by a triangular system that satisfies a linear growth condition with a polynomial output‐dependent rate. The result presented in this article has incorporated and generalized recent advances on robust output feedback control of nonlinear systems with output uncertainty, all of them are required to satisfy a linear growth condition with a constant rate. A nonidentifier‐based universal controller is proposed with a high gain estimator, rather than observer, whose gain is updated in a dynamic fashion. It is shown that a single dynamic gain is sufficient for dealing with the unknown parameter at the system output and the system parametric uncertainty simultaneously.