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Fault Detection for Nonlinear Systems with Unknown Input
Author(s) -
Luo Zhen,
Fang Huajing
Publication year - 2013
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.658
Subject(s) - nonlinear system , fault detection and isolation , control theory (sociology) , filter (signal processing) , fault (geology) , kalman filter , state (computer science) , process (computing) , computer science , recursive filter , signal (programming language) , algorithm , filter design , artificial intelligence , control (management) , physics , quantum mechanics , root raised cosine filter , seismology , computer vision , programming language , geology , operating system
Abstract This paper extends the problem of fault detection for linear discrete‐time systems with unknown input to the nonlinear system. A nonlinear recursive filter is developed where the estimation of the state and the input are interconnected. Unknown input which can be any type of signal was obtained by least‐squares unbiased estimation and the state estimation problem is transformed into a standard unscented K alman filter ( UKF ) problem. By testing the mean of the innovation process, a real‐time fault detection approach is proposed. Simulations are provided to demonstrate the effectiveness of the theoretical results.