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Adaptive Neural Network Control for Nonlinear Systems Based on Approximation Errors

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3972))

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Abstract

A stable adaptive neural network control approach is proposed in this paper for uncertain nonlinear strict-feedback systems based on backstepping. The key assumptions are that the neural network approximation errors satisfy certain bounding conditions. By a special scheme, the controller singularity problem is avoided perfectly. The proposed scheme improves the control performance of systems and extends the application scope of nonlinear systems. The overall neural network control systems guarantee that all the signals of the systems are uniformly ultimately bounded and the tracking error converges to a small neighborhood of zero by suitably choosing the design parameter.

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Author information

Authors and Affiliations

  1. School of Electronic and Information Engineering, Dalian University of Technology, Dalian, 116023, China

    Yan-Jun Liu & Wei Wang

Authors
  1. Yan-Jun Liu
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  2. Wei Wang
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd., 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Liu, YJ., Wang, W. (2006). Adaptive Neural Network Control for Nonlinear Systems Based on Approximation Errors. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_124

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  • DOI: https://doi.org/10.1007/11760023_124

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34437-7

  • Online ISBN: 978-3-540-34438-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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