Please use this identifier to cite or link to this item: http://rportal.lib.ntnu.edu.tw:80/handle/77345300/31938
Title: RGA-based On-Line Tuning of BMF Fuzzy-Neural Networks for Adaptive Control of Uncertain Nonlinear Systems
Authors: 國立臺灣師範大學電機工程學系
Y.-G. Leu
W.-Y. Wang
I-H. Li
Issue Date: 1-Jun-2009
Publisher: Elsevier
Abstract: In this paper, an RGA-based indirect adaptive fuzzy-neural controller (RIAFC) for uncertain nonlinear systems is proposed by using a reduced-form genetic algorithm (RGA). Both the control points of B-spline membership functions (BMFs) and the weighting factors of the adaptive fuzzy-neural controller are tuned on-line via the RGA approach. Each gene represents an adjustable parameter of the BMF fuzzy-neural network with real number components. For the purpose of on-line tuning these parameters and evaluating the stability of the closed-loop system, a special fitness function is included in the RGA approach. In addition, in order to guarantee that the system states are confined to the safe region, a supervisory controller is incorporated into the RIAFC. To illustrate the feasibility and applicability of the proposed method, two examples of nonlinear systems controlled by the RIAFC are demonstrated.
URI: http://rportal.lib.ntnu.edu.tw/handle/77345300/31938
ISSN: 0925-2312
Other Identifiers: ntnulib_tp_E0604_01_016
Appears in Collections:教師著作

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