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International Conference on Information, Electronic and Computer Science (ICIECS 2010 E-BOOK)

Zibo,China,11.26-11.28,2010

ISBN: 978-1-935068-42-6 Scientific Research Publishing, USA

E-Book 2224pp Pub. Date: November 2010

Category: Computer Science & Communications

Price: $360

Title: Wavelet Neural Network Based Adaptive Observer Design for a Class of MIMO Nonlinear Systems
Source: International Conference on Information, Electronic and Computer Science (ICIECS 2010 E-BOOK) (pp 1104-1108)
Author(s): Yonghong Zhu, Dept. of Mechanical & Electronic Engineering, Jingdezhen Ceramic Institute, Jingdezhen, China
Wenzhong Gao, Dept. of Electrical & Computer Engineering, University of Denver, Denver, America
Abstract: State estimation for uncertain systems affected by external noises is an important problem in control theory. This paper deals with a state observation problem for a class of multi-input multi-output non-affine nonlinear systems with the relative degree 2. Wavelet neural network based adaptive observer is proposed to estimate the states of the classes of systems. Wavelet neural network is used to approximate unknown nonlinearity. A learning law containing an adaptive adjustment rate, is suggested to imply the stability condition for the free parameters of the observer. The weight of the adaptive law proposed is adjusted online. It is proved that all error signals of the observer and the weight error are uniformly ultimately bounded(UBB) in the sense of Lyapunov by a appropriate choice of observer parameters. In the end, a simulation example is presented to illustrate the effectiveness and the applicability of the suggested method.
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