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Adaptive Neural Compensation Control for Input-Delay Nonlinear Systems by Passive Approach

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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

This paper focuses on the design of passive controller with adaptive neural compensation for uncertain strict-feedback nonlinear systems with input-delay. For local linearization model, the delay-dependent γ-passive control is presented. Then, γ-passive control law of local linear model is decomposed as the virtual control of sub-systems by backstepping. In order to compensate the nonlinear dynamics, the adaptive neural model is proposed. The NN weights are turned on-line by Lyapunov stability theory with no prior training. The design procedure of whole systems is a combination of local γ-passive control and adaptive neural network compensation techniques.

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Author information

Authors and Affiliations

  1. School of Automatization, Harbin Engineering University, 150001, Harbin, China

    Zhandong Yu, Xiren Zhao & Xiuyan Peng

Authors
  1. Zhandong Yu
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  2. Xiren Zhao
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  3. Xiuyan Peng
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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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Yu, Z., Zhao, X., Peng, X. (2006). Adaptive Neural Compensation Control for Input-Delay Nonlinear Systems by Passive Approach. 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_127

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  • DOI: https://doi.org/10.1007/11760023_127

  • 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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