Linear system identification via an asymptotically stable observerThis paper presents a formulation for identification of linear multivariable systems from single or multiple sets of input-output data. The system input-output relationship is expressed in terms of an observer, which is made asymptotically stable by an embedded eigenvalue assignment procedure. The prescribed eigenvalues for the observer may be real, complex, mixed real and complex, or zero. In this formulation, the Markov parameters of the observer are identified from input-output data. The Markov parameters of the actual system are then recovered from those of the observer, and used to obtain a state space model of the system by standard realization techniques. The basic mathematical formulation is derived, and numerical examples using simulated noise-free data are presented to illustrate the proposed method.
Document ID
19910065069
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Phan, Minh (NASA Langley Research Center Hampton, VA, United States)
Horta, Lucas G. (NASA Langley Research Center Hampton, VA, United States)
Juang, Jer-Nan (NASA Langley Research Center Hampton, VA, United States)
Longman, Richard W. (NASA Langley Research Center Hampton, VA, United States)
Date Acquired
August 14, 2013
Publication Date
January 1, 1991
Subject Category
Cybernetics
Report/Patent Number
AIAA PAPER 91-2734
Meeting Information
Meeting: AIAA Guidance, Navigation and Control Conference