Fuzzy neural network-based model reference adaptive inverse control for induction machines

Shao, Z; Zhan, Y; Guo, Y

Fuzzy neural network-based model reference adaptive inverse control for induction machines

Shao, Z Zhan, Y Guo, Y

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Publication Type:: Conference Proceeding
Citation:: 2009 International Conference on Applied Superconductivity and Electromagnetic Devices, ASEMD 2009, 2009, pp. 56 - 59
Issue Date:: 2009-11-30

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Field	Value	Language
dc.contributor.author	Shao, Z	en_US
dc.contributor.author	Zhan, Y	en_US
dc.contributor.author	Guo, Y https://orcid.org/0000-0001-6182-0684	en_US
dc.date.issued	2009-11-30	en_US
dc.identifier.citation	2009 International Conference on Applied Superconductivity and Electromagnetic Devices, ASEMD 2009, 2009, pp. 56 - 59	en_US
dc.identifier.isbn	9781424436873	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. ©2009 IEEE.	en_US
dc.relation.ispartof	2009 International Conference on Applied Superconductivity and Electromagnetic Devices, ASEMD 2009	en_US
dc.relation.isbasedon	10.1109/ASEMD.2009.5306695	en_US
dc.title	Fuzzy neural network-based model reference adaptive inverse control for induction machines	en_US
dc.type	Conference Proceeding
utslib.for	0913 Mechanical Engineering	en_US
dc.location.activity	Chengdu, China	en_US
pubs.embargo.period	Not known	en_US
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 Electrical and Data Engineering
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

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. ©2009 IEEE.

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