Robust telemanipulator control using a partitioned neural network architecture

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dc.contributor.author Tzafestas Spyros, G en
dc.contributor.author Prokopiou Platon, A en
dc.contributor.author Tzafestas Costas, S en
dc.date.accessioned 2014-03-01T02:41:30Z
dc.date.available 2014-03-01T02:41:30Z
dc.date.issued 1997 en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/30494
dc.subject Control Problem en
dc.subject Dynamic Model en
dc.subject Learning Algorithm en
dc.subject Multilayer Perceptron en
dc.subject Sliding Mode Control en
dc.subject Neural Network en
dc.subject.other Computer simulation en
dc.subject.other Heuristic methods en
dc.subject.other Learning algorithms en
dc.subject.other Mathematical models en
dc.subject.other Neural networks en
dc.subject.other Online systems en
dc.subject.other Remote control en
dc.subject.other Robot learning en
dc.subject.other Robustness (control systems) en
dc.subject.other Dynamic models en
dc.subject.other Teleoperator control scheme en
dc.subject.other Manipulators en
dc.title Robust telemanipulator control using a partitioned neural network architecture en
heal.type conferenceItem en
heal.identifier.primary 10.1109/ICNN.1997.614161 en
heal.identifier.secondary http://dx.doi.org/10.1109/ICNN.1997.614161 en
heal.publicationDate 1997 en
heal.abstract In this paper the control problem of telemanipulators is considered under the condition that they are subject to modeling and other uncertainties of considerable levels. The design is based on the S. Lee and H. S. Lee teleoperator control scheme, which is modified so as to be able to compensate the uncertainties, and is implemented using a partitioned multilayer perception neural network. Several subnetworks are used each one identifying a term of the manipulator's dynamic model. A new learning algorithm is proposed which distributes the learning error to each subnetwork and enables online training. Several simulation results are provided, which show the robustness ability by the partitioned neurocontroller, and compare it with the results obtained through sliding mode control. en
heal.publisher IEEE, Piscataway, NJ, United States en
heal.journalName IEEE International Conference on Neural Networks - Conference Proceedings en
dc.identifier.doi 10.1109/ICNN.1997.614161 en
dc.identifier.volume 3 en
dc.identifier.spage 1755 en
dc.identifier.epage 1760 en

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