NDLI: Development & Comparative Analysis of an Adaptive Fuzzy Logic LFC control model for Gain Scheduling using Fuzzy Logic & Adaptive Fuzzy Techniques

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Development & Comparative Analysis of an Adaptive Fuzzy Logic LFC control model for Gain Scheduling using Fuzzy Logic & Adaptive Fuzzy Techniques

Content Provider	Semantic Scholar
Author	Kaur, Amanpreet Singh, Parampreet Gupta, Divya Kumar, Ashish Singh, Inderpal
Copyright Year	2010
Abstract	In this paper, the recent data based artificially intelligent techniques like fuzzy and neural network have been customized and used .The application/case study has been taken. Fuzzy provides a robust inference mechanism with no learning and adaptability and artificial neural network provides learning and adaptability. Artificial neural networks and fuzzy systems have been successfully applied to the LFC problem with rather promising results. In this paper, an adaptive fuzzy gain scheduling scheme for conventional PI controllers has been simulated and tested for off-nominal operating conditions. From the simulation and the result obtained in this paper, it has been shown that the proposed adaptive fuzzy logic controller offers better performance than fixed gain controllers& fuzzy gain controller. Comparative analysis of percentage error of gain using fuzzy& adaptive fuzzy has also been done in this paper. The full text of the article is not available in the cache. Kindly refer the IJCA digital library at www.ijcaonline.org for the complete article. In case, you face problems while downloading the full-text, please send a mail to editor at editor@ijcaonline.org Er. Ashish Kumar Mechanical Engg. Deptt RIET, Rail Majra Er.Inderpal Singh Electrical Engg. Deptt BBSBEC, Fatehgarh
Starting Page	1
Ending Page	15
Page Count	15
File Format	PDF HTM / HTML
DOI	10.5120/308-475
Volume Number	1
Alternate Webpage(s)	http://www.ijcaonline.org/allpdf/pxc387475.pdf
Alternate Webpage(s)	https://www.ijcaonline.org/archives/volume1/number14/308-475?format=pdf
Alternate Webpage(s)	https://doi.org/10.5120/308-475
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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