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

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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Acknowledgments

The authors would like to thank the valuable comments and also appreciate the constructive suggestions from the anonymous referees. This research was supported by the Natural Science Foundation of China under Grant 61074014 and 60874056; The Chinese National Basic Research 973 Program under Grant 2011CB302801; Macau Science and Technology Development Foundation under Grant 008/2010/A1; The Science Foundation of Educational Department of Liaoning Province under Grant L2010181; The Natural Science Foundation of Liaoning Province under Grant 20102095.

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Authors and Affiliations

  1. School of Sciences, Liaoning University of Technology, 121001, Jinzhou, Liaoning, China
    Guo-Xing Wen & Yan-Jun Liu
  2. Faculty of Science and Technology, University of Macau, Macau, Av. Padre Tomás Pereira, S.J., Taipa, Macau, S.A.R., China
    C. L. Philip Chen

Authors

  1. Guo-Xing Wen
  2. Yan-Jun Liu
  3. C. L. Philip Chen

Corresponding author

Correspondence toYan-Jun Liu.

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Wen, GX., Liu, YJ. & Philip Chen, C.L. Direct adaptive robust NN control for a class of discrete-time nonlinear strict-feedback SISO systems.Neural Comput & Applic 21, 1423–1431 (2012). https://doi.org/10.1007/s00521-011-0596-4

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