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System identification methods using parameter estimation / a survey

โœ Scribed by H Unbehauen


Publisher
Elsevier Science
Year
1985
Weight
897 KB
Volume
12
Category
Article
ISSN
0066-4138

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๐Ÿ“œ SIMILAR VOLUMES


A SURVEY OF CURRENT INERTIA PARAMETER ID
โœ CARSTEN SCHEDLINSKI; MICHAEL LINK ๐Ÿ“‚ Article ๐Ÿ“… 2001 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 442 KB

The knowledge of the 10 inertia parameters mass, centre of gravity and inertia tensor (moments and products of inertia) of mechanical systems is of great interest whenever the dynamic behaviour is signi"cantly governed by these parameters. In this paper, a survey of currently available inertia param

REAL-TIME MODAL PARAMETER ESTIMATION USI
โœ F. Tasker; A. Bosse; S. Fisher ๐Ÿ“‚ Article ๐Ÿ“… 1998 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 207 KB

This article describes the underlying theory of a newly developed algorithm for online modal parameter identification. These online subspace estimation methods use eigenanalysis for data filtering, and are derived from a recent multi-input, multi-output batch algorithm. One method is obtained by der

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โœ A. Bosse; F. Tasker; S. Fisher ๐Ÿ“‚ Article ๐Ÿ“… 1998 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 365 KB

This article describes the underlying theory and hardware implementation of a newly developed algorithm for online modal parameter identification. An online modal parameter estimation algorithm using subspace methods is applied to both model and experimental data for a 4-m laboratory truss structure