Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7550
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dc.contributor.authorJiao, Junen_US
dc.contributor.authorChen, Wu-Weien_US
dc.contributor.authorProf. LEUNG Kwong Saken_US
dc.contributor.authorLi, Shao-Wenen_US
dc.contributor.authorWang, Ji-Xianen_US
dc.contributor.authorCheung, William K. C.en_US
dc.contributor.authorLin, Marie C.en_US
dc.date.accessioned2023-03-23T04:41:34Z-
dc.date.available2023-03-23T04:41:34Z-
dc.date.issued2008-
dc.identifier.citation2008 IEEE Congress on Evolutionary Computation, CEC 2008, pp. 3968 - 3973, Article number 4631337en_US
dc.identifier.isbn978-142441823-7-
dc.identifier.urihttp://hdl.handle.net/20.500.11861/7550-
dc.description.abstractAiming at Automated Guided Vehicle (AGV) dynamic model characteristics, a Variable Structure Control based on genetic algorithm (GA) and least square-support vector machine (LS-SVM) was designed. Parameters, predetermined by conventional reaching law, were regulated by LS-SVM online. It was shown that system shattering is eliminated. Simulation results indicated that this method possesses the advantages of higher precision, greater adaptability and robustness, as compared to the conventional Variable Structure Control methods. © 2008 IEEE.en_US
dc.language.isoenen_US
dc.relation.ispartof2008 IEEE Congress on Evolutionary Computation, CEC 2008en_US
dc.titleIntelligent Variable Structure Control for Automated Guided Vehicleen_US
dc.typeConference Paperen_US
dc.identifier.doi10.1109/CEC.2008.4631337-
item.fulltextNo Fulltext-
crisitem.author.deptDepartment of Applied Data Science-
Appears in Collections:Applied Data Science - Publication
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