Robust telemanipulator control using a partitioned neural network architecture

Tzafestas Spyros, G; Prokopiou Platon, A; Tzafestas Costas, S

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