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Identification of linear multivariable systems from a single set of data by identification of observers with assigned real eigenvaluesA formulation is presented for identification of linear multivariable from a single set of input-output data. The identification method is formulated with the mathematical framework of learning identifications, by extension of the repetition domain concept to include shifting time intervals. This method contrasts with existing learning approaches that require data from multiple experiments. In this method, the system input-output relationship is expressed in terms of an observer, which is made asymptotically stable by an embedded real eigenvalue assignment procedure. Through this relationship, the Markov parameters of the observer are identified. The Markov parameters of the actual system are recovered from those of the observer, and then used to obtain a state space model of the system by standard realization techniques. The basic mathematical formulation is derived, and numerical examples presented to illustrate.
Document ID
19910012262
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Phan, Minh (NASA Langley Research Center Hampton, VA., United States)
Juang, Jer-Nan (NASA Langley Research Center Hampton, VA., United States)
Longman, Richard W. (Columbia Univ. New York, NY., United States)
Date Acquired
September 6, 2013
Publication Date
March 1, 1991
Subject Category
Structural Mechanics
Report/Patent Number
NAS 1.15:102669
NASA-TM-102669
Meeting Information
Meeting: Structures, Structural Dynamics and Materials Conference
Location: Baltimore, MD
Country: United States
Start Date: April 8, 1991
End Date: April 10, 1991
Accession Number
91N21575
Funding Number(s)
PROJECT: RTOP 590-14-21-01
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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