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Adaptive Neural Network Control for Switched System with Unknown Nonlinear Part by Using Backstepping Approach: SISO Case

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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

In this paper, we address, in a backstepping way, stabilization problem for a class of switched nonlinear systems whose subsystem with trigonal structure by using neural network. An adaptive neural network switching control design is given. Backsteppping, domination and adaptive bounding design technique are combined to construct adaptive neural network stabilizer and switching law. Based on common Lyapunov function approach, the stabilization of the resulting closed-loop systems is proved.

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Author information

Authors and Affiliations

  1. Department of Automatic Control, Southeast University, Nanjing, 210096, P.R. China

    Fei Long, Shumin Fei, Zhumu Fu & Shiyou Zheng

  2. School of Information Engineering, Guizhou University, Guiyang, 550000, P.R. China

    Fei Long

Authors
  1. Fei Long
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  2. Shumin Fei
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  3. Zhumu Fu
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  4. Shiyou Zheng
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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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Long, F., Fei, S., Fu, Z., Zheng, S. (2006). Adaptive Neural Network Control for Switched System with Unknown Nonlinear Part by Using Backstepping Approach: SISO Case. 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_125

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  • DOI: https://doi.org/10.1007/11760023_125

  • 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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