Intelligent control for DC-DC power converter with recurrent fuzzy neural network approach

dc.citation.epage	462	en_US
dc.citation.spage	457	en_US
dc.contributor.author	Hsu, Chun-Fei	en_US
dc.contributor.author	Lee, Tsu-Tian	en_US
dc.contributor.author	Wen, Yao-Wei	en_US
dc.contributor.author	Ding, Fu-Shan	en_US
dc.contributor.department	電控工程研究所	zh_TW
dc.contributor.department	Institute of Electrical and Control Engineering	en_US
dc.date.accessioned	2014-12-08T15:24:46Z
dc.date.available	2014-12-08T15:24:46Z
dc.date.issued	2006	en_US
dc.description.abstract	For many years, the control approaches for the dc-dc power converters are limited to PI controller structures. However, it gives the overshoot in output voltage as the rise time of response is reduced. To tackle this problem, an adaptive recurrent fuzzy neural network (ARFNN) control system is developed in this paper. The on-line adaptive laws of the ARFNN control scheme are derived based on the Lyapunov stability theorem, so that the stability of the system can be guaranteed. Experimental results show that the proposed ARFNN control scheme can achieve good regulation performances.	en_US
dc.identifier.isbn	978-0-7803-9488-9	en_US
dc.identifier.issn	1098-7584	en_US
dc.identifier.journal	2006 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-5	en_US
dc.identifier.uri	https://ir.lib.nycu.edu.tw/handle/11536/17212
dc.identifier.wosnumber	WOS:000244063600066
dc.language.iso	en_US	en_US
dc.title	Intelligent control for DC-DC power converter with recurrent fuzzy neural network approach	en_US
dc.type	Proceedings Paper	en_US

Files

Now showing 1 - 1 of 1