Applications of a gradient flow algorithm for parameter identification of non-linear systems in continuous-time

Persistent Link:
http://hdl.handle.net/10150/282637
Title:
Applications of a gradient flow algorithm for parameter identification of non-linear systems in continuous-time
Author:
Shin, Jae Ho, 1967-
Issue Date:
1998
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
An efficient methodology for parameter identification is developed for general multi-degree of freedom linear or nonlinear systems in continuous time. The new methodology is based on a gradient flow algorithm and demonstrated to be useful in identifying unknown parameters for the systems defined by both linear and nonlinear differential equations. The new methodology identifies the unknown parameters by solving a system of differential equations rather than the conventional post-data fitting analysis. It is named the trajectory gradient integral flow (TGIF) algorithm. For the cases of stable, one-dimensional linear systems, the asymptotic stability of the TGIF algorithm is guaranteed in the neighborhood of the operating point. For higher order linear or nonlinear systems, certain criteria for stability are developed using the eigenvalue analysis and the Routh-Hurwitz stability criteria. A well-known system identification result is that any method works the best with non-steady, non-periodic data set that is driven by randomized inputs, however this is not an essential requirement with the TGIF algorithm. In fact, it is possible to perform efficient parameter identification with the TGIF algorithm using an unit step input or a simple sine input. Improvements over previous approaches include: (1) the new methodology is easy to apply for nonlinear systems, (2) it works well with a simple unit step or sinusoidal inputs as well as bounded (control) inputs, (3) it demonstrates a reasonable large "domain of attraction", (4) it can be applied for either "on-line" or "off-line" parameter identification processes.
Type:
text; Dissertation-Reproduction (electronic)
Keywords:
Engineering, Aerospace.; Engineering, Electronics and Electrical.; Engineering, System Science.
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Aerospace and Mechanical Engineering
Degree Grantor:
University of Arizona
Advisor:
Vincent, Thomas L.

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titleApplications of a gradient flow algorithm for parameter identification of non-linear systems in continuous-timeen_US
dc.creatorShin, Jae Ho, 1967-en_US
dc.contributor.authorShin, Jae Ho, 1967-en_US
dc.date.issued1998en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractAn efficient methodology for parameter identification is developed for general multi-degree of freedom linear or nonlinear systems in continuous time. The new methodology is based on a gradient flow algorithm and demonstrated to be useful in identifying unknown parameters for the systems defined by both linear and nonlinear differential equations. The new methodology identifies the unknown parameters by solving a system of differential equations rather than the conventional post-data fitting analysis. It is named the trajectory gradient integral flow (TGIF) algorithm. For the cases of stable, one-dimensional linear systems, the asymptotic stability of the TGIF algorithm is guaranteed in the neighborhood of the operating point. For higher order linear or nonlinear systems, certain criteria for stability are developed using the eigenvalue analysis and the Routh-Hurwitz stability criteria. A well-known system identification result is that any method works the best with non-steady, non-periodic data set that is driven by randomized inputs, however this is not an essential requirement with the TGIF algorithm. In fact, it is possible to perform efficient parameter identification with the TGIF algorithm using an unit step input or a simple sine input. Improvements over previous approaches include: (1) the new methodology is easy to apply for nonlinear systems, (2) it works well with a simple unit step or sinusoidal inputs as well as bounded (control) inputs, (3) it demonstrates a reasonable large "domain of attraction", (4) it can be applied for either "on-line" or "off-line" parameter identification processes.en_US
dc.typetexten_US
dc.typeDissertation-Reproduction (electronic)en_US
dc.subjectEngineering, Aerospace.en_US
dc.subjectEngineering, Electronics and Electrical.en_US
dc.subjectEngineering, System Science.en_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineAerospace and Mechanical Engineeringen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorVincent, Thomas L.en_US
dc.identifier.proquest9829377en_US
dc.identifier.bibrecord.b38555335en_US
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