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dc.contributor.authorRegister, Andrew H.en_US
dc.contributor.authorAlford, Cecil Orieen_US
dc.contributor.authorBook, Wayne Johnen_US
dc.date.accessioned2011-05-25T19:48:51Z
dc.date.available2011-05-25T19:48:51Z
dc.date.issued1996-04
dc.identifier.citationRegister, A., W.J. Book, and C.O. Alford, “Artificial Neural Network Control of a Nonminimum Phase Mechanical System”, 1996 IEEE International Conference on Robotics and Automation (ICRA), Minneapolis, Minnesota, April 1996, Vol. 2, 1935-1940.en_US
dc.identifier.isbn0-7803-2988-0
dc.identifier.issn1050-4729
dc.identifier.urihttp://hdl.handle.net/1853/38985
dc.description©1996 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.en_US
dc.descriptionPresented at the 1996 IEEE International Conference on Robotics and Automation (ICRA), April 22-28, 1996, Minneapolis, MN.en_US
dc.descriptionDOI: 10.1109/ROBOT.1996.506994en_US
dc.description.abstractA single-link flexible manipulator with a rotary actuator at one end and a mass at the other is modeled using the Lagrangian method coupled with an assumed modes vibration model. A SIMO state space model is developed by linearizing the equations of motion and simplified by neglecting natural damping. Laplace domain pole-zero plots between torque input and tip position show nonmzmmum phase behavior. Nonminimum phase behavior causes difficulty for both conventional and artificial neural network (ANN) inversemodel control. The most promising ANN method for the control of flexible manipulators does not appear to converge to a solution when the system is lightly damped. To overcome this limitation, a modified cost junction is proposed. Simulations show that the ANN is able to converge to a solution even in the case of no damping. The modified approach fails, however, for beams exceeding some critical length measure. Identification of the critical length and proposals for extending the result are discussed.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectRobotsen_US
dc.subjectNeural networksen_US
dc.subjectFlexible manipulatorsen_US
dc.subjectLagrangian methodsen_US
dc.titleArtificial neural network control of a nonminimum phase, single-flexible-linken_US
dc.typeProceedingsen_US
dc.typePost-printen_US
dc.contributor.corporatenameGeorgia Tech Research Instituteen_US
dc.contributor.corporatenameGeorgia Institute of Technology. School of Mechanical Engineeringen_US
dc.contributor.corporatenameGeorgia Institute of Technology. School of Electrical Engineeringen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Center for Robotics and Intelligent Machinesen_US
dc.publisher.originalInstitute of Electrical and Electronics Engineersen_US


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