NDLI: Predictive control simulation study based on T-S fuzzy model

Content Provider	IEEE Xplore Digital Library
Author	Du Shi-Jie Shen Qing-Bo
Copyright Year	2011
Description	Author affiliation: Department of Information and Control Engineering, Liaoning University of Petroleum & Chemical Technology, Fushun 113001, China (Du Shi-Jie; Shen Qing-Bo)
Abstract	A T-S fuzzy model was established for nonlinear system by a fuzzy identification method, fuzzy predictive control was developed by combining with generalized predictive control. In this paper, the fuzzy identification method was based on fast fuzzy clustering, premise parameter identification using multi-step random sampling method, fuzzy C clustering method, parameter identification of the conclusions using least squares. This identification method divided fuzzy clustering into two parts, compared with the previous method, it can greatly shorten the time and has a high recognition accuracy. In terms of the nonlinear system, T-S fuzzy model has a good description of features, combined with the moving optimization of generalized predictive control to achieve effective control of nonlinear systems. The simulation results show that the algorithm is effective.
Starting Page	299
Ending Page	302
File Size	95601
Page Count	4
File Format	PDF
ISBN	9781457706776
ISSN	21612927
e-ISBN	9789881725592
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-22
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Chinese Assoc of Automati
Subject Keyword	C-means clustering Predictive models Prediction algorithms Generalized Predictive Control Mathematical model Nonlinear systems Modeling Equations T-S model Predictive control
Content Type	Text
Resource Type	Article

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