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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Journal of optimization theory and applications 79 (1993), S. 59-86 
    ISSN: 1573-2878
    Keywords: System identification ; observer identification ; pole placement ; state space realization ; Markov parameters ; observer Markov parameters
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract This 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 corresponding to a deadbeat observer. In this formulation, the Markov parameters of the observer are first identified from input-output data. The Markov parameters of the actual system are then recovered from those of the observer and used to realize a state space model of the system. The basic mathematical formulation is derived, and numerical examples are presented to illustrate the proposed method.
    Type of Medium: Electronic Resource
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  • 2
    Publication Date: 1993-10-01
    Print ISSN: 0022-3239
    Electronic ISSN: 1573-2878
    Topics: Mathematics
    Published by Springer
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  • 3
    Publication Date: 2019-07-13
    Description: This paper reviews recent results in learning control and learning system identification, with particular emphasis on discrete-time formulation, and their relation to adaptive theory. Related continuous-time results are also discussed. Among the topics presented are proportional, derivative, and integral learning controllers, time-domain formulation of discrete learning algorithms. Newly developed techniques are described including the concept of the repetition domain, and the repetition domain formulation of learning control by linear feedback, model reference learning control, indirect learning control with parameter estimation, as well as related basic concepts, recursive and non-recursive methods for learning identification.
    Keywords: CYBERNETICS
    Type: International Conference on the Dynamics of Flexible Structures in Space; May 15, 1990 - May 18, 1990; Cranfield
    Format: text
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  • 4
    Publication Date: 2019-07-13
    Description: In the last few years various methods of identifying structural dynamics models from modal testing data have appeared. This paper presents a comparison of four of these algorithms: the Eigensystem Realization Algorithm (ERA), the modified version ERA/DC where DC indicates that it makes use of data correlations, the Q-Markov Cover algorithm, and an algorithm due to Moonen, DeMoor, Vandenberghe and Vandewalle. The comparison is made using a five mode computer model of the 20 meter Mini-Mast truss structure at NASA Langley Research Center, and various noise levels are superimposed to produce simulated data. The results show that for the example considered ERA/DC generally gives the best results; that ERA/DC is always at least as good as ERA which is shown to be a special case of ERA/DC; that Q-Markov requires the use of significantly more data than ERA/DC to produce comparable results; and that in some situations Q-Markov cannot produce comparable results.
    Keywords: STRUCTURAL MECHANICS
    Type: AIAA PAPER 91-0947 , AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference; Apr 08, 1991 - Apr 10, 1991; Baltimore, MD; United States
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