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Neural Computing and Applications

Erschienen in:

01.09.2012 | Original Article

Direct adaptive robust NN control for a class of discrete-time nonlinear strict-feedback SISO systems

verfasst von: Guo-Xing Wen, Yan-Jun Liu, C. L. Philip Chen

Erschienen in: Neural Computing and Applications | Ausgabe 6/2012

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Abstract

In this paper, a direct adaptive neural network control algorithm based on the backstepping technique is proposed for a class of uncertain nonlinear discrete-time systems in the strict-feedback form. The neural networks are utilized to approximate unknown functions, and a stable adaptive neural network controller is synthesized. The fact that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded is proven and the tracking error can converge to a small neighborhood of zero by choosing the design parameters appropriately. Compared with the previous research for discrete-time systems, the proposed algorithm improves the robustness of the systems. A simulation example is employed to illustrate the effectiveness of the proposed algorithm.

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Titel: Direct adaptive robust NN control for a class of discrete-time nonlinear strict-feedback SISO systems
verfasst von: Guo-Xing Wen
Yan-Jun Liu
C. L. Philip Chen
Publikationsdatum: 01.09.2012
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 6/2012
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-011-0596-4

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