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Accuracy-enhancing methods for balancing-related frequency-weighted model and controller reduction

Varga, Andras and Anderson, Brain D.O. (2003) Accuracy-enhancing methods for balancing-related frequency-weighted model and controller reduction. Automatica, 39 (3), pp. 919-927.

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Abstract

We consider the solution of the balancing-related frequency-weighted model and controller reduction problems using accuracy enhanced numerical algorithms. We propose first new stability-enforcing choices of the frequency-weighted grammians which can guarantee the stability of reduced models for two-sided frequency weights. Then we show that for the frequency-weighted controller reduction problems with standard stability and performance-enforcing frequency weights the computation of the frequency-weighted grammians can be done by solving reduced order Lyapunov equations. For both frequency-weighted model and controller reduction problems we indicate how to compute the grammians directly in terms of their Cholesky factors. This allows the extension of the square-root and balancing-free accuracy-enhancing techniques to the frequency-weighted case.

Document Type:Article
Additional Information:LIDO-Berichtsjahr=2003,
Title:Accuracy-enhancing methods for balancing-related frequency-weighted model and controller reduction
Authors:
AuthorsInstitution or Email of Authors
Varga, AndrasUNSPECIFIED
Anderson, Brain D.O.Australian National University
Date:March 2003
Journal or Publication Title:Automatica
Volume:39
Page Range:pp. 919-927
Status:Published
Keywords:Model reduction, Balancing, Frequency-weighting, Controller reduction, Numerical algorithm
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:other
HGF - Program Themes:other
DLR - Research area:Aeronautics
DLR - Program:L - no assignement
DLR - Research theme (Project):L -- no assignement
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics
Deposited By: elib DLR-Beauftragter
Deposited On:20 Sep 2007
Last Modified:12 Dec 2013 20:02

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