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Real-time control of manufacturing cells using dynamic neural networks

Rovithakis, George A., 1968-, Gaganis Vasileios, Perrakis, Stelios, Christodoulou Manolis

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URIhttp://purl.tuc.gr/dl/dias/CB3186C6-260D-4C31-A913-3BB895079536-
Identifierhttps://doi.org/10.1016/S0005-1098(98)00139-3-
Identifierhttp://www.sciencedirect.com/science/article/pii/S0005109898001393-
Languageen-
Extent11 pagesen
TitleReal-time control of manufacturing cells using dynamic neural networksen
CreatorRovithakis, George A., 1968-en
CreatorGaganis Vasileiosen
CreatorΓαγανης Βασιλειοςel
CreatorPerrakis, Steliosen
CreatorChristodoulou Manolisen
CreatorΧριστοδουλου Μανωληςel
PublisherElsevieren
Content SummaryIn this paper, a control aspect of the non-acyclic FMS scheduling problem is considered. Based on a dynamic neural network model derived herein, an adaptive, continuous time neural network controller is constructed. The actual dispatching times are determined from the continuous control input discretization. The controller is capable of driving system production to the required demand and guaranteeing system stability and boundedness of all signals in the closed-loop system. Modeling errors and discretization effects are taken into account thus rendering the controller robust. A case study demonstrates the efficiency of the proposed technique.en
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-11-07-
Date of Publication1999-
SubjectArtificial neural networksen
SubjectNets, Neural (Computer science)en
SubjectNetworks, Neural (Computer science)en
SubjectNeural nets (Computer science)en
Subjectneural networks computer scienceen
Subjectartificial neural networksen
Subjectnets neural computer scienceen
Subjectnetworks neural computer scienceen
Subjectneural nets computer scienceen
Bibliographic CitationG. A. Rovithakis, V. I. Gaganis, S. E. Perrakis and M. A. Christodoulou, “Real-time control of manufacturing cells using dynamic neural networks”, Automatica, vol. 35, no. 1, pp. 139-149, 1999. doi:10.1016/S0005-1098(98)00139-3en

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