Payload adaptive control of a flexible manipulator using neural networks

Persistent Link:
http://hdl.handle.net/10150/278203
Title:
Payload adaptive control of a flexible manipulator using neural networks
Author:
Askew, Craig Steven, 1967-
Issue Date:
1992
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:
Flexible manipulators provide significant advantages over the commonly-used rigid robots due to their lightweight properties, but an accurate control of these manipulators is more difficult to attain, and it is especially demanding in task executions involving changing payloads. This thesis addresses the problem of payload adaptive control of flexible manipulators. The nonlinear model describing the manipulator dynamics is completely derived and is then used for an accurate computer simulation of the flexible manipulator motions. Payload identification is implemented by using a novel neural network approach to identify distinct payload classes from tip deflection patterns which result from different payloads. The identification procedure is then used to select a controller which best meets the control objectives specifying hub speed and maximum tip deflection. Two distinct controller synthesis procedures, one using a pole-placement design and one employing a variable structure technique, are developed. The merits of payload adaptive control are shown by several simulation experiments.
Type:
text; Thesis-Reproduction (electronic)
Keywords:
Engineering, Electronics and Electrical.; Engineering, Industrial.; Engineering, System Science.; Artificial Intelligence.
Degree Name:
M.S.
Degree Level:
masters
Degree Program:
Graduate College
Degree Grantor:
University of Arizona
Advisor:
Sundareshan, Malur K.

Full metadata record

DC FieldValue Language
dc.language.isoen_USen_US
dc.titlePayload adaptive control of a flexible manipulator using neural networksen_US
dc.creatorAskew, Craig Steven, 1967-en_US
dc.contributor.authorAskew, Craig Steven, 1967-en_US
dc.date.issued1992en_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.abstractFlexible manipulators provide significant advantages over the commonly-used rigid robots due to their lightweight properties, but an accurate control of these manipulators is more difficult to attain, and it is especially demanding in task executions involving changing payloads. This thesis addresses the problem of payload adaptive control of flexible manipulators. The nonlinear model describing the manipulator dynamics is completely derived and is then used for an accurate computer simulation of the flexible manipulator motions. Payload identification is implemented by using a novel neural network approach to identify distinct payload classes from tip deflection patterns which result from different payloads. The identification procedure is then used to select a controller which best meets the control objectives specifying hub speed and maximum tip deflection. Two distinct controller synthesis procedures, one using a pole-placement design and one employing a variable structure technique, are developed. The merits of payload adaptive control are shown by several simulation experiments.en_US
dc.typetexten_US
dc.typeThesis-Reproduction (electronic)en_US
dc.subjectEngineering, Electronics and Electrical.en_US
dc.subjectEngineering, Industrial.en_US
dc.subjectEngineering, System Science.en_US
dc.subjectArtificial Intelligence.en_US
thesis.degree.nameM.S.en_US
thesis.degree.levelmastersen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorSundareshan, Malur K.en_US
dc.identifier.proquest1350765en_US
dc.identifier.bibrecord.b25469587en_US
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