RGA-based On-Line Tuning of BMF Fuzzy-Neural Networks for Adaptive Control of Uncertain Nonlinear Systems

No Thumbnail Available

Date

2009-06-01

Authors

Y.-G. Leu
W.-Y. Wang
I-H. Li

Journal Title

Journal ISSN

Volume Title

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.

Description

Keywords

Citation

Collections