Artificial neural network control of a nonminimum phase, single-flexible-link

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dc.contributor.author Register, Andrew H. en_US
dc.contributor.author Alford, Cecil Orie en_US
dc.contributor.author Book, Wayne John en_US
dc.date.accessioned 2011-05-25T19:48:51Z
dc.date.available 2011-05-25T19:48:51Z
dc.date.issued 1996-04
dc.identifier.citation Register, 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.isbn 0-7803-2988-0
dc.identifier.issn 1050-4729
dc.identifier.uri http://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.description Presented at the 1996 IEEE International Conference on Robotics and Automation (ICRA), April 22-28, 1996, Minneapolis, MN. en_US
dc.description DOI: 10.1109/ROBOT.1996.506994 en_US
dc.description.abstract A 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.iso en_US en_US
dc.publisher Georgia Institute of Technology en_US
dc.subject Robots en_US
dc.subject Neural networks en_US
dc.subject Flexible manipulators en_US
dc.subject Lagrangian methods en_US
dc.title Artificial neural network control of a nonminimum phase, single-flexible-link en_US
dc.type Proceedings en_US
dc.type Post-print en_US
dc.contributor.corporatename Georgia Tech Research Institute en_US
dc.contributor.corporatename Georgia Institute of Technology. School of Mechanical Engineering en_US
dc.contributor.corporatename Georgia Institute of Technology. School of Electrical Engineering en_US
dc.contributor.corporatename Georgia Institute of Technology. Center for Robotics and Intelligent Machines en_US
dc.publisher.original Institute of Electrical and Electronics Engineers en_US


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