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Iterative learning scheme to design intermittent fault estimators for nonlinear systems with parameter uncertainties and measurement noise
Author(s) -
Feng Li,
Xu Shuiqing,
Chai Yi,
Yang Zhimin,
Zhang Ke
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.2880
Subject(s) - estimator , iterative learning control , control theory (sociology) , observer (physics) , convergence (economics) , nonlinear system , lyapunov function , iterative method , fault (geology) , mathematics , computer science , noise (video) , mathematical optimization , artificial intelligence , statistics , physics , control (management) , quantum mechanics , seismology , geology , economics , image (mathematics) , economic growth
Summary In this paper, an iterative learning estimator is proposed to deal with period intermittent fault estimation problem in a class of nonlinear uncertain systems. First, state observer is designed for state reconstruction, followed by the Lyapunov function is presented to guarantee the convergence of the system output. Then, the iterative learning scheme–based fault estimator is presented to track the fault signal and the optimal function is established to ensure tracking error convergence. Moreover, linear matrix inequalities and Schur complements are utilized to obtain the sufficient conditions for the existence of iterative learning estimator. Compared with the existing results, error augmented systems should not satisfy the strictly positive realness assumption. Besides, previous state estimation error is used for current fault estimation such that to improve the estimating accuracy. Finally, 2 numerical examples are given to illustrate the effectiveness and validity of the proposed methods.