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http://dx.doi.org/10.5391/JKIIS.2005.15.6.720

Indirect Adaptive Control of Nonlinear Systems Using a EKF Learning Algorithm Based Wavelet Neural Network  

Kim Kyoung-Joo (연세대학교 전기전자공학과)
Choi Yoon Ho (경기대학교 전자공학부)
Park Jin Bae (연세대학교 전기전자공학과)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.15, no.6, 2005 , pp. 720-729 More about this Journal
Abstract
In this paper, we design the indirect adaptive controller using Wavelet Neural Network(WNN) for unknown nonlinear systems. The proposed indirect adaptive controller using WNN consists of identification model and controller. Here, the WNN is used in both Identification model and controller The WNN has advantage of indicating the location in both time and frequency simultaneously, and has faster convergence than MLPN and RBFN. There are several training methods for WNN, such as GD, GA, DNA, etc. In this paper, we present the Extended Kalman Filter(EKF) based training method. Although it is computationally complex, this algorithm updates parameters consistent with previous data and usually converges in a few iterations. Finally, ore illustrate the effectiveness of our method through computer simulations for the Buffing system and the one-link rigid robot manipulator. From the simulation results, we show that the indirect adaptive controller using the EKF method has better performance than the GD method.
Keywords
Wavelet Neural Network; Extended Kalman Filter; Indirect Adaptive Control; Nonlinear System;
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