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Linear system identification via backward-time observer modelsThis paper presents an algorithm to identify a state-space model of a linear system using a backward-time approach. The procedure consists of three basic steps. First, the Markov parameters of a backward-time observer are computed from experimental input-output data. Second, the backward-time observer Markov parameters are decomposed to obtain the backward-time system Markov parameters (backward-time pulse response samples) from which a backward-time state-space model is realized using the Eigensystem Realization Algorithm. Third, the obtained backward-time state space model is converted to the usual forward-time representation. Stochastic properties of this approach will be discussed. Experimental results are given to illustrate when and to what extent this concept works.
Document ID
19930067430
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Juang, Jer-Nan
(NASA Langley Research Center Hampton, VA, United States)
Phan, Minh
(Lockheed Engineering & Sciences Co. Hampton, VA, United States)
Date Acquired
August 16, 2013
Publication Date
January 1, 1993
Publication Information
Publication: In: AIAA Guidance, Navigation and Control Conference, Monterey, CA, Aug. 9-11, 1993, Technical Papers. Pt. 3 (A93-51301 22-63)
Publisher: American Institute of Aeronautics and Astronautics
Subject Category
Cybernetics
Report/Patent Number
AIAA PAPER 93-3838
Accession Number
93A51427
Distribution Limits
Public
Copyright
Other

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