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Adaptive Neural Control for a Class of Large-Scale Pure-Feedback Nonlinear Systems

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Advances in Neural Networks – ISNN 2013 (ISNN 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7952))

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Abstract

This paper considers the problem of adaptive neural decentralized control for pure-feedback nonlinear interconnected large-scale systems. Radical basis function (RBF) neural networks are used to model packaged unknown nonlinearities and backstepping is used to construct decentralized controller. The proposed control scheme can guarantee that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded. A numerical example is provided to illustrate the effectiveness of the suggested approach.

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Author information

Authors and Affiliations

  1. School of Mathematics and Physics, Bohai University, Jinzhou, 121000, Liaoning, P.R. China

    Huanqing Wang

  2. Institute of Complexity Science, Qingdao University, Qingdao, 266071, Shandong, P.R. China

    Huanqing Wang, Bing Chen & Chong Lin

Authors
  1. Huanqing Wang
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  2. Bing Chen
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  3. Chong Lin
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Editor information

Editors and Affiliations

  1. School of Information and Communication Engineering, Dalian University of Technology, A 530, Chuangxinyuan Building, 116023, Dalian, China

    Chengan Guo

  2. Institute of Automation, Chinese Academy of Sciences, 100864, Beijing, China

    Zeng-Guang Hou

  3. School of Automation, Huazhong University of Science and Technology, 430074, Wuhan, China

    Zhigang Zeng

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© 2013 Springer-Verlag Berlin Heidelberg

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Wang, H., Chen, B., Lin, C. (2013). Adaptive Neural Control for a Class of Large-Scale Pure-Feedback Nonlinear Systems. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7952. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39068-5_12

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  • DOI: https://doi.org/10.1007/978-3-642-39068-5_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39067-8

  • Online ISBN: 978-3-642-39068-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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