Please use this identifier to cite or link to this item: http://rportal.lib.ntnu.edu.tw:80/handle/77345300/31942
Title: An On-Line Robust and Adaptive T-S Fuzzy-Neural Controller for More General Unknown Systems
Authors: 國立臺灣師範大學電機工程學系
W.-Y. Wang
Y.-H. Chien
I-H. Li
Issue Date: 1-Mar-2008
Publisher: 中華民國模糊學會
Abstract: This paper proposes a novel method of on-line modeling via the Takagi-Sugeno (T-S) fuzzy-neural model and robust adaptive control for a class of general unknown nonaffine nonlinear systems with external disturbances. Although studies about adaptive T-S fuzzy-neural controllers have been made on some nonaffine nonlinear systems, little is known on the more complicated and general nonlinear systems. Compared with the previous approaches, the contribution of this paper is an investigation of the more general unknown nonaffine nonlinear systems using on-line adaptive T-S fuzzy-neural controllers. Instead of modeling these unknown systems directly, the T-S fuzzy-neural model approximates a so-called virtual linearized system (VLS), with modeling errors and external disturbances. We prove that the closed-loop system controlled by the proposed controller is robust stable and the effect of all the unmodeled dynamics, modeling errors and external disturbances on the tracking error is attenuated under mild assumptions. To illustrate the effectiveness and applicability of the proposed method, simulation results are given in this paper
URI: http://rportal.lib.ntnu.edu.tw/handle/77345300/31942
ISSN: 1562-2480
Other Identifiers: ntnulib_tp_E0604_01_020
Appears in Collections:教師著作

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