Fuzzy Neural Network-based Model Reference Adaptive Inverse Control for Induction Machines

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dc.contributor.author Shao, Z
dc.contributor.author Zhan, Y
dc.contributor.author Guo, Y
dc.contributor.editor Jin, J
dc.date.accessioned 2010-05-28T09:58:43Z
dc.date.issued 2009-01
dc.identifier.citation Proceedings of IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, 2009, pp. 56 - 59
dc.identifier.isbn 978-1-4244-3687-3
dc.identifier.other E1 en_US
dc.identifier.uri http://hdl.handle.net/10453/10698
dc.description.abstract In this paper, because the induction machines are described as the plants of highly nonlinear and parameters timevarying, in order to obtain a very well control performances that a conventional model reference adaptive inverse control (MRAIC) can not be gotten, a fuzzy neural network-based model reference adaptive inverse control strategy for induction motors is presented based on the rotor field oriented motion model of induction machines. The fuzzy neural network control (FNNC) is incorporated into the model reference adaptive control (MRAC), a fuzzy basis function network controller (FBNC) and a fuzzy neural network identifier (FNNI) for asynchronous motors adjustable speed system are designed. The proposed controller for asynchronous machines resolves the shortage of MRAC, and employs the advantages of FNNC and MRAC. Simulation results show that the proposed control strategy is of the feasibility, correctness and effectiveness.
dc.format Esha Dutt
dc.publisher IEEE
dc.relation.hasversion Accepted manuscript version en_US
dc.relation.isbasedon 10.1109/ASEMD.2009.5306695
dc.title Fuzzy Neural Network-based Model Reference Adaptive Inverse Control for Induction Machines
dc.type Conference Proceeding
dc.parent Proceedings of IEEE International Conference on Applied Superconductivity and Electromagnetic Devices
dc.journal.number en_US
dc.publocation USA en_US
dc.identifier.startpage 56 en_US
dc.identifier.endpage 59 en_US
dc.cauo.name FEIT.Faculty of Engineering & Information Technology en_US
dc.conference Verified OK en_US
dc.conference IEEE International Conference on Applied Superconductivity and Electromagnetic Devices
dc.for 0913 Mechanical Engineering
dc.personcode 990817
dc.percentage 100 en_US
dc.classification.name Mechanical Engineering en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom IEEE International Conference on Applied Superconductivity and Electromagnetic Devices en_US
dc.date.activity 20090925 en_US
dc.date.activity 2009-09-25
dc.location.activity Chengdu, China en_US
dc.description.keywords induction machine; mochine dynamic model; fuzzy neural network control (FNNC); model reference adaptive control (MRAC) en_US
dc.description.keywords induction machine
dc.description.keywords mochine dynamic model
dc.description.keywords fuzzy neural network control (FNNC)
dc.description.keywords model reference adaptive control (MRAC)
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology/School of Elec, Mech and Mechatronic Systems
utslib.copyright.status Open Access
utslib.copyright.date 2015-04-15 12:23:47.074767+10
utslib.collection.history General (ID: 2)


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