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Actuator fault estimation based on generalized learning observer for quasi‐linear parameter varying systems
Author(s) -
ZetinaRios Israel I.,
OsorioGordillo GloriaL.,
VargasMéndez Rodolfo A.,
MadrigalEspinosa Guadalupe,
AstorgaZaragoza CarlosM.
Publication year - 2021
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.3229
Subject(s) - observer (physics) , control theory (sociology) , actuator , lyapunov function , stability (learning theory) , mathematics , linear matrix inequality , linear system , fault detection and isolation , computer science , mathematical optimization , artificial intelligence , nonlinear system , control (management) , mathematical analysis , machine learning , physics , quantum mechanics
Summary This article presents a generalized learning observer (GLO) design for the simultaneous estimation of states and actuator faults for polytopic quasi‐linear parameter varying systems. The proposed approach is based on the use of a GLO, which generalized the existing results on the proportional‐integral observers. Conditions of existence and stability of the observer are given through the stability analysis in the sense of Lyapunov. Its design is obtained in terms of a set of linear matrix inequalities. The performance of the proposed method is evaluated by simulation in a one‐link‐flexible joint robot system.