Dynamic performance improvement of an ultra-lift Luo DC–DC converter by using a type-2 fuzzy neural controller

Sharifian, A; Fathi Sasansara, S; Ghadi, MJ; Ghavidel, S; Li, L; Zhang, J

Dynamic performance improvement of an ultra-lift Luo DC–DC converter by using a type-2 fuzzy neural controller

Sharifian, A Fathi Sasansara, S Ghadi, MJ

Ghavidel, S Li, L

Zhang, J

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Publication Type:: Journal Article
Citation:: Computers and Electrical Engineering, 2018, 69 pp. 171 - 182
Issue Date:: 2018-07-01

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Field	Value	Language
dc.contributor.author	Sharifian, A	en_US
dc.contributor.author	Fathi Sasansara, S	en_US
dc.contributor.author	Ghadi, MJ https://orcid.org/0000-0002-5564-5055	en_US
dc.contributor.author	Ghavidel, S	en_US
dc.contributor.author	Li, L https://orcid.org/0000-0001-8485-2216	en_US
dc.contributor.author	Zhang, J https://orcid.org/0000-0003-2616-6722	en_US
dc.date.available	2020-07-31T19:04:55Z
dc.date.issued	2018-07-01	en_US
dc.identifier.citation	Computers and Electrical Engineering, 2018, 69 pp. 171 - 182	en_US
dc.identifier.issn	0045-7906	en_US
dc.identifier.uri	http://hdl.handle.net/10453/129664
dc.description.abstract	© 2018 Due to the uncertainty associated with the structure and electrical elements of DC–DC converters and the nonlinear performance of these modules, designing an effective controller is highly complicated and also technically challenging. This paper employs a new control approach based on type-2 fuzzy neural controller (T2FNC) in order to improve the dynamic response of an ultra-lift Luo DC–DC converter under different operational conditions. The proposed controller can rapidly stabilize the output voltage of converter to expected values by tuning the converter switching duty cycle. This controller can tackle the uncertainties associated with the structure of converters, measured control signals and measuring devices. Moreover, a new intelligent method based on firefly algorithm is applied to tune the parameters of T2FNC. In order to demonstrate the effectiveness of the proposed control approach, the proposed controller is compared to PI and fuzzy controllers under different operational conditions. Results validate efficiency of proposed T2FNC.	en_US
dc.relation.ispartof	Computers and Electrical Engineering	en_US
dc.relation.isbasedon	10.1016/j.compeleceng.2018.05.012	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Electrical & Electronic Engineering	en_US
dc.title	Dynamic performance improvement of an ultra-lift Luo DC–DC converter by using a type-2 fuzzy neural controller	en_US
dc.type	Journal Article
utslib.citation.volume	69	en_US
utslib.for	0803 Computer Software	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0906 Electrical and Electronic 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/Students
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	69	en_US

Abstract:

© 2018 Due to the uncertainty associated with the structure and electrical elements of DC–DC converters and the nonlinear performance of these modules, designing an effective controller is highly complicated and also technically challenging. This paper employs a new control approach based on type-2 fuzzy neural controller (T2FNC) in order to improve the dynamic response of an ultra-lift Luo DC–DC converter under different operational conditions. The proposed controller can rapidly stabilize the output voltage of converter to expected values by tuning the converter switching duty cycle. This controller can tackle the uncertainties associated with the structure of converters, measured control signals and measuring devices. Moreover, a new intelligent method based on firefly algorithm is applied to tune the parameters of T2FNC. In order to demonstrate the effectiveness of the proposed control approach, the proposed controller is compared to PI and fuzzy controllers under different operational conditions. Results validate efficiency of proposed T2FNC.

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http://hdl.handle.net/10453/129664