Nonconvex spectral optimization algorithms for reduced-order H<inf>∞</inf> LPV-LFT controllers

Shi, Y; Tuan, HD; Apkarian, P

Nonconvex spectral optimization algorithms for reduced-order H<inf>∞</inf> LPV-LFT controllers

Shi, Y

Tuan, HD

Apkarian, P

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Publication Type:: Journal Article
Citation:: International Journal of Robust and Nonlinear Control, 2017, 27 (18), pp. 4421 - 4442
Issue Date:: 2017-12-01

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Field	Value	Language
dc.contributor.author	Shi, Y https://orcid.org/0000-0003-2302-4980	en_US
dc.contributor.author	Tuan, HD https://orcid.org/0000-0003-0292-6061	en_US
dc.contributor.author	Apkarian, P	en_US
dc.date.issued	2017-12-01	en_US
dc.identifier.citation	International Journal of Robust and Nonlinear Control, 2017, 27 (18), pp. 4421 - 4442	en_US
dc.identifier.issn	1049-8923	en_US
dc.identifier.uri	http://hdl.handle.net/10453/115296
dc.description.abstract	Copyright © 2017 John Wiley & Sons, Ltd. A novel sequential semi-definite programming method is developed for optimization subject to rank constraints on matrix-valued nonlinear functions of matrix decision variables, which arise in reduced-order linear parameter varying-linear fractional transformational control synthesis. The global convergence of the method is easily proven without any step size control. An intensive simulation shows the clear advantage of the proposed method over the state-of-the-art nonlinear matrix inequality solvers. Copyright © 2017 John Wiley & Sons, Ltd.	en_US
dc.relation.ispartof	International Journal of Robust and Nonlinear Control	en_US
dc.relation.isbasedon	10.1002/rnc.3805	en_US
dc.subject.classification	Industrial Engineering & Automation	en_US
dc.title	Nonconvex spectral optimization algorithms for reduced-order H<inf>∞</inf> LPV-LFT controllers	en_US
dc.type	Journal Article
utslib.citation.volume	18	en_US
utslib.citation.volume	27	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0913 Mechanical 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 Computer Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access
pubs.issue	18	en_US
pubs.publication-status	Published	en_US
pubs.volume	27	en_US

Abstract:

Copyright © 2017 John Wiley & Sons, Ltd. A novel sequential semi-definite programming method is developed for optimization subject to rank constraints on matrix-valued nonlinear functions of matrix decision variables, which arise in reduced-order linear parameter varying-linear fractional transformational control synthesis. The global convergence of the method is easily proven without any step size control. An intensive simulation shows the clear advantage of the proposed method over the state-of-the-art nonlinear matrix inequality solvers. Copyright © 2017 John Wiley & Sons, Ltd.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/115296