Please use this identifier to cite or link to this item: http://rportal.lib.ntnu.edu.tw:80/handle/77345300/31958
Title: An online GA-based output-feedback direct adaptive fuzzy-neural controller for uncertain nonlinear systems
Authors: 國立臺灣師範大學電機工程學系
W.-Y. Wang
C.-Y. Cheng
Y.-G. Leu
Issue Date: 1-Feb-2004
Publisher: IEEE Systems, Man, and Cybernetics Society
Abstract: In this paper, we propose a novel design of a GA-based output-feedback direct adaptive fuzzy-neural controller (GODAF controller) for uncertain nonlinear dynamical systems. The weighting factors of the direct adaptive fuzzy-neural controller can successfully be tuned online via a GA approach. Because of the capability of genetic algorithms (GAs) in directed random search for global optimization, one is used to evolutionarily obtain the optimal weighting factors for the fuzzy-neural network. Specifically, we use a reduced-form genetic algorithm (RGA) to adjust the weightings of the fuzzy-neural network. In RGA, a sequential-search -based crossover point (SSCP) method determines a suitable crossover point before a single gene crossover actually takes place so that the speed of searching for an optimal weighting vector of the fuzzy-neural network can be improved. A new fitness function for online tuning the weighting vector of the fuzzy-neural controller is established by the Lyapunov design approach. A supervisory controller is incorporated into the GODAF controller to guarantee the stability of the closed-loop nonlinear system. Examples of nonlinear systems controlled by the GODAF controller are demonstrated to illustrate the effectiveness of the proposed method.
URI: http://rportal.lib.ntnu.edu.tw/handle/77345300/31958
ISSN: 1083-4419�
Other Identifiers: ntnulib_tp_E0604_01_036
Appears in Collections:教師著作

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.