Note
College of Engineering and Computer Science
Member of
Contributors
Publisher
Florida Atlantic University
Date Issued
1992
Description
The dissertation focuses on robot manipulator dynamic modeling, and inertial
and kinematic parameters identification problem. An automatic dynamic parameters
derivation symbolic algorithm is presented. This algorithm provides the linearly
independent dynamic parameters set. It is shown that all the dynamic parameters are
identifiable when the trajectory is persistently exciting. The parameters set satisfies
the necessary condition of finding a persistently exciting trajectory. Since in practice the system data matrix is corrupted with noise, conventional
estimation methods do not converge to the true values. An error bound is given for
Kalman filters. Total least squares method is introduced to obtain unbiased
estimates.
Simulations studies are presented for five particular identification methods.
The simulations are performed under different noise levels.
Observability problems for the inertial and kinematic parameters are
investigated. U%wer certain conditions all L%wearly Independent Parameters
derived from are observable.
The inertial and kinematic parameters can be categorized into three parts
according to their influences on the system dynamics. The dissertation gives an
algorithm to classify these parameters.
and kinematic parameters identification problem. An automatic dynamic parameters
derivation symbolic algorithm is presented. This algorithm provides the linearly
independent dynamic parameters set. It is shown that all the dynamic parameters are
identifiable when the trajectory is persistently exciting. The parameters set satisfies
the necessary condition of finding a persistently exciting trajectory. Since in practice the system data matrix is corrupted with noise, conventional
estimation methods do not converge to the true values. An error bound is given for
Kalman filters. Total least squares method is introduced to obtain unbiased
estimates.
Simulations studies are presented for five particular identification methods.
The simulations are performed under different noise levels.
Observability problems for the inertial and kinematic parameters are
investigated. U%wer certain conditions all L%wearly Independent Parameters
derived from are observable.
The inertial and kinematic parameters can be categorized into three parts
according to their influences on the system dynamics. The dissertation gives an
algorithm to classify these parameters.
Language
Type
Form
Extent
244 p.
Subject (Topical)
Identifier
12291
Additional Information
College of Engineering and Computer Science
FAU Electronic Theses and Dissertations Collection
Thesis (Ph.D.)--Florida Atlantic University, 1992.
Date Backup
1992
Date Text
1992
Date Issued (EDTF)
1992
Extension
FAU
FAU
admin_unit="FAU01", ingest_id="ing1508", creator="staff:fcllz", creation_date="2007-07-18 20:16:39", modified_by="super:SPATEL", modification_date="2011-01-06 13:08:38"
IID
FADT12291
Issuance
monographic
Organizations
Attributed name: Florida Atlantic University
Attributed name: College of Engineering and Computer Science
Person Preferred Name
Xu, Hua.
Graduate College
Physical Description
244 p.
application/pdf
Title Plain
Derivation and identification of linearly parametrized robot manipulator dynamic models
Use and Reproduction
Copyright © is held by the author, with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
http://rightsstatements.org/vocab/InC/1.0/
Origin Information
1992
monographic
Boca Raton, Fla.
Florida Atlantic University
Physical Location
Florida Atlantic University Libraries
Place
Boca Raton, Fla.
Sub Location
Digital Library
Title
Derivation and identification of linearly parametrized robot manipulator dynamic models
Other Title Info
Derivation and identification of linearly parametrized robot manipulator dynamic models