Static output feedback stabilization for fractional-order systems in T-S fuzzy models

doi:10.1016/j.neucom.2016.08.085

Details of Research Outputs

Status	已发表Published
Title	Static output feedback stabilization for fractional-order systems in T-S fuzzy models
Creator	Lin, Chong 1; Chen, Bing 1; Wang, Qingguo2
Date Issued	2016
Source Publication	Neurocomputing
ISSN	0925-2312
Volume	218 Pages:354-358
Abstract	This paper investigates the stabilization problem of fractional-order Takagi-Sugeno (T-S) fuzzy systems via static output feedback control. It provides a direct linear matrix inequality method for the control design, which overcomes the drawback of two-step design approaches and avoids requiring special structures in design matrices. The present method serves as an important alternative for both fractional-order and integer-order systems, and illustrative examples show the merits of the delivered results. © 2016 Elsevier B.V.
Keyword	Fractional-order systems Stabilization Static output feedback T-S fuzzy systems
DOI	10.1016/j.neucom.2016.08.085
URL	View source
Indexed By	SCIE
Language	英语English
WOS Research Area	Computer Science
WOS Subject	Computer Science, Artificial Intelligence
WOS ID	WOS:000388053700039
Citation statistics	Cited Times:61[WOS] [WOS Record] [Related Records in WOS]
Document Type	Journal article
Identifier	http://repository.uic.edu.cn/handle/39GCC9TT/3735
Collection	Research outside affiliated institution
Affiliation	1.Institute of Complexity Science, Qingdao University, Qingdao, 266071, China 2.School of Electrical Engineering, University of Johannesburg, Johannesburg, South Africa
Recommended Citation GB/T 7714	Lin, Chong,Chen, Bing,Wang, Qingguo. Static output feedback stabilization for fractional-order systems in T-S fuzzy models[J]. Neurocomputing, 2016, 218: 354-358.
APA	Lin, Chong, Chen, Bing, & Wang, Qingguo. (2016). Static output feedback stabilization for fractional-order systems in T-S fuzzy models. Neurocomputing, 218, 354-358.
MLA	Lin, Chong,et al."Static output feedback stabilization for fractional-order systems in T-S fuzzy models". Neurocomputing 218(2016): 354-358.

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