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Reduced‐order Observer for State‐dependent Coefficient Factorized Nonlinear Systems
Author(s) -
OrnelasTellez Fernando,
Alanis Alma Y.,
Rios Jorge D.,
Graff Mario
Publication year - 2019
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.1808
Subject(s) - observer (physics) , control theory (sociology) , nonlinear system , state observer , state vector , convergence (economics) , mathematics , factorization , state (computer science) , computer science , control (management) , algorithm , artificial intelligence , physics , classical mechanics , quantum mechanics , economics , economic growth
This paper presents a reduced‐order observer for state‐dependent coefficient factorized nonlinear systems. By considering that a partial knowledge of the state vector is available from measurements, estimating the full state vector may be unnecessary, which consequently reduces the order of the observer and thus avoids unnecessary implementation issues. In this manuscript, the asymptotic convergence of the proposed reduced‐order observer is established when an adequate state‐dependent factorization for the nonlinear system is obtained. This paper demonstrates the ease of synthesizing reduced‐order observers for state‐dependent coefficient factorized nonlinear systems. The effectiveness of the proposed observer is illustrated in real‐time for the optimal tracking control of a linear induction motor.