Wavelet Neural Network Based Adaptive Robust Control for a Class of MIMO Nonlinear Systems

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Wavelet neural network based adaptive robust output tracking control approach is proposed for a class of MIMO nonlinear systems with unknown nonlinearities in this paper. A wavelet network is constructed as an alternative to a neural network to approximate unknown nonlinearities of the classes of systems. The proposed WNN adaptive law is used to compensate the dynamic inverse errors of the classes of systems. The robust control law is designed to attenuate the effects of approximate errors and external disturbances. It is proved that the controller proposed can guarantee that all the signals in the closed-loop control system are uniformly ultimately bounded (UUB) in the sense of Lyapunov. In the end, a simulation example is presented to illustrate the effectiveness and the applicability of the suggested method.

Info:

Periodical:

Advanced Materials Research (Volumes 383-390)

Edited by:

Wu Fan

Pages:

290-296

DOI:

10.4028/www.scientific.net/AMR.383-390.290

Citation:

Y. H. Zhu and W. Z. Gao, "Wavelet Neural Network Based Adaptive Robust Control for a Class of MIMO Nonlinear Systems", Advanced Materials Research, Vols. 383-390, pp. 290-296, 2012

Online since:

November 2011

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Price:

$35.00

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