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Derivation and identification of linearly parametrized robot manipulator dynamic models

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Date Issued:
1992
Summary:
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.
Title: Derivation and identification of linearly parametrized robot manipulator dynamic models.
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Name(s): Xu, Hua.
Florida Atlantic University, Degree grantor
Roth, Zvi S., Thesis advisor
Zilouchian, Ali, Thesis advisor
College of Engineering and Computer Science
Department of Computer and Electrical Engineering and Computer Science
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Issuance: monographic
Date Issued: 1992
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 244 p.
Language(s): English
Summary: 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.
Identifier: 12291 (digitool), FADT12291 (IID), fau:9194 (fedora)
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): College of Engineering and Computer Science
Thesis (Ph.D.)--Florida Atlantic University, 1992.
Subject(s): Algorithms
Manipulators (Mechanism)
Robots--Control systems
Held by: Florida Atlantic University Libraries
Persistent Link to This Record: http://purl.flvc.org/fcla/dt/12291
Sublocation: Digital Library
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.
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU
Is Part of Series: Florida Atlantic University Digital Library Collections.