A Robust Design Optimization Method for Electromagnetic Devices with Interval Uncertainties

Ma, B; Zheng, J; Lei, G; Zhu, J; Guo, Y; Wu, J

A Robust Design Optimization Method for Electromagnetic Devices with Interval Uncertainties

Ma, B

Zheng, J

Lei, G

Zhu, J

Guo, Y

Wu, J

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Magnetics, 2018, 54 (11)
Issue Date:: 2018-11-01

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Field	Value	Language
dc.contributor.author	Ma, B https://orcid.org/0000-0003-2779-8369	en_US
dc.contributor.author	Zheng, J https://orcid.org/0000-0002-1480-3862	en_US
dc.contributor.author	Lei, G https://orcid.org/0000-0002-8369-6802	en_US
dc.contributor.author	Zhu, J https://orcid.org/0000-0002-9763-4047	en_US
dc.contributor.author	Guo, Y https://orcid.org/0000-0001-6182-0684	en_US
dc.contributor.author	Wu, J https://orcid.org/0000-0002-4925-4242	en_US
dc.date.issued	2018-11-01	en_US
dc.identifier.citation	IEEE Transactions on Magnetics, 2018, 54 (11)	en_US
dc.identifier.issn	0018-9464	en_US
dc.identifier.uri	http://hdl.handle.net/10453/129321
dc.description.abstract	© 1965-2012 IEEE. High reliability is usually demanded for the design of electromagnetic devices. Interval uncertainties widely exist in the robust optimization problems, and the bounds of the design parameters can be only obtained instead of their specific distributions. To solve this kind of problem efficiently, this paper presents a robust optimization approach based on the Chebyshev interval method to estimate the extreme values of the constraints and objectives. To demonstrate the effectiveness of the method, the Testing Electromagnetic Analysis Methods workshop problem 22 and a brushless dc wheel motor optimization benchmark problem are investigated with interval uncertainties of the relevant parameters. Deterministic and robust optimizations considering the worst case with uniform sampling are also conducted for comparison. The optimization results illustrate the effectiveness of the proposed method.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0773858
dc.relation.ispartof	IEEE Transactions on Magnetics	en_US
dc.relation.isbasedon	10.1109/TMAG.2018.2839882	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Applied Physics	en_US
dc.title	A Robust Design Optimization Method for Electromagnetic Devices with Interval Uncertainties	en_US
dc.type	Journal Article
utslib.citation.volume	11	en_US
utslib.citation.volume	54	en_US
utslib.for	0203 Classical Physics	en_US
utslib.for	0915 Interdisciplinary Engineering	en_US
utslib.for	02 Physical Sciences	en_US
utslib.for	09 Engineering	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
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CCET - Centre for Clean Energy Technology
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access	*
pubs.issue	11	en_US
pubs.publication-status	Published	en_US
pubs.volume	54	en_US

Abstract:

© 1965-2012 IEEE. High reliability is usually demanded for the design of electromagnetic devices. Interval uncertainties widely exist in the robust optimization problems, and the bounds of the design parameters can be only obtained instead of their specific distributions. To solve this kind of problem efficiently, this paper presents a robust optimization approach based on the Chebyshev interval method to estimate the extreme values of the constraints and objectives. To demonstrate the effectiveness of the method, the Testing Electromagnetic Analysis Methods workshop problem 22 and a brushless dc wheel motor optimization benchmark problem are investigated with interval uncertainties of the relevant parameters. Deterministic and robust optimizations considering the worst case with uniform sampling are also conducted for comparison. The optimization results illustrate the effectiveness of the proposed method.

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http://hdl.handle.net/10453/129321