Implementation of Adaptive Neural Networks Controller for NXT SCARA Robot System

dc.contributor.authorAbouzaid, Wessam M. F.
dc.contributor.authorSallam, Elsayed. A.
dc.date.accessioned2017-01-03T11:36:26Z
dc.date.available2017-01-03T11:36:26Z
dc.date.issued2017-01-03
dc.description.abstractSeveral neural network controllers for robotic manipulators have been developed during the last decades due to their capability to learn the dynamic properties and the improvements in the global stability of the system. In this paper, an adaptive neural controller has been designed with self learning to resolve the problems caused by using a classical controller. A comparison between the improved unsupervised adaptive neural network controller and the P controller for the NXT SCARA robot system is done, and the result shows the improvement of the self learning controller to track the determined trajectory of robotic automated controllers with uncertainties. Implementation and practical results were designed to guarantee online real-time.hu_HU
dc.identifier.doi10.17667/riim.2017.1/3.hu_HU
dc.identifier.issn2064-9622hu_HU
dc.identifier.issue1hu_HU
dc.identifier.urihttp://hdl.handle.net/2437/233496
dc.identifier.volume4hu_HU
dc.language.isoenhu_HU
dc.publisherDUPresshu_HU
dc.publisher.placeDebrecenhu_HU
dc.subjectNonlinear MIMO Systemshu_HU
dc.subjectADALINEhu_HU
dc.subjecttrajectory designhu_HU
dc.subjectNXT SCARA Modelhu_HU
dc.titleImplementation of Adaptive Neural Networks Controller for NXT SCARA Robot Systemhu_HU
dc.typeArticlehu_HU
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