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dc.contributor.authorBoutalis, Yiannis S.
dc.contributor.authorMoise, Adrian
dc.contributor.authorMertzios, B. G.
dc.date.accessioned2015-12-10T10:33:15Z
dc.date.available2015-12-10T10:33:15Z
dc.date.issued2001
dc.identifier.issn1221-454X
dc.identifier.urihttp://10.11.10.50/xmlui/handle/123456789/3719
dc.descriptionThe Annals of "Dunarea de Jos" University of Galatien_US
dc.description.abstractIn this paper we are studying the Cartesian space robot manipulator control problem by using Neural Networks (NN). Although NN compensation for model uncertainties has been traditionally carried out by modifying the joint torque/force of the robot, it is also possible to achieve the same objective by using the NN to modify other quantities of the controller. We present and evaluate four different NN controller designs to achieve disturbance rejection for an uncertain system. The design perspectives are dependent on the compensated position by NN. There are four quantities that can be compensated: torque t , force F, control input U and the input trajectory Xd. By defining a unified training signal all NN control schemes have the same goal of minimizing the same objective functions. We compare the four schemes in respect to their control performance and the efficiency of the NN designs, which is demonstrated via simulations.en_US
dc.language.isoenen_US
dc.publisher"Dunarea de Jos" University of Galatien_US
dc.subjectManipulation Roboten_US
dc.subjectNeural Networken_US
dc.subjectCartesian Space Controlen_US
dc.titleNeural Network Schemes in Cartesian Space Control of Robot Manipulatorsen_US
dc.typeArticleen_US


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