Real-time control of manufacturing cells using dynamic neural networks

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dc.contributor.author Rovithakis, GA en
dc.contributor.author Gaganis, VI en
dc.contributor.author Perrakis, SE en
dc.contributor.author Christodoulou, MA en
dc.date.accessioned 2014-03-01T01:48:42Z
dc.date.available 2014-03-01T01:48:42Z
dc.date.issued 1999 en
dc.identifier.issn 00051098 en
dc.identifier.uri http://hdl.handle.net/123456789/25572
dc.relation.uri http://www.scopus.com/inward/record.url?eid=2-s2.0-0032740014&partnerID=40&md5=ce20513d0f2fcf672fdcda2dd2d4a127 en
dc.subject Adaptive control en
dc.subject Manufacturing systems en
dc.subject Neural networks en
dc.subject.other Closed loop control systems en
dc.subject.other Control equipment en
dc.subject.other Neural networks en
dc.subject.other Real time systems en
dc.subject.other Robustness (control systems) en
dc.subject.other Stability en
dc.subject.other Real time control systems en
dc.subject.other Flexible manufacturing systems en
dc.title Real-time control of manufacturing cells using dynamic neural networks en
heal.type journalArticle en
heal.publicationDate 1999 en
heal.abstract In 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. © 1999 Elsevier Science Ltd. All rights reserved. en
heal.journalName Automatica en
dc.identifier.volume 35 en
dc.identifier.issue 1 en
dc.identifier.spage 139 en
dc.identifier.epage 149 en

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