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Robust Friction Compensation for Servo System Based on LuGre Model with Uncertain Static Parameters

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Advances in Neural Networks - ISNN 2004 (ISNN 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3174))

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Abstract

A new adaptive robust friction compensation for servo system based on luGre model is proposed. Considered the uncertainty of steady state parameters in the friction model, a RBF neural network is adopted to learn the nonlinear friction-velocity relationship in steady state. The bristle displacement is observed using the output of the network. Nonlinear adaptive robust control laws are designed based on backstepping theory to compensate the unknown system parameters. System robustness and asymptotic results is proved and shown in simulation results.

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References

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Author information

Authors and Affiliations

  1. Yanshan University, Qinhuangdao, 066004, China

    Lixin Wei

  2. Hebei University, Baoding, 071002, China

    Xia Wang & Hongrui Wang

Authors
  1. Lixin Wei
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  2. Xia Wang
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  3. Hongrui Wang
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Editor information

Editors and Affiliations

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

    Fu-Liang Yin

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

    Jun Wang

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

    Chengan Guo

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© 2004 Springer-Verlag Berlin Heidelberg

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Wei, L., Wang, X., Wang, H. (2004). Robust Friction Compensation for Servo System Based on LuGre Model with Uncertain Static Parameters. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_32

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  • DOI: https://doi.org/10.1007/978-3-540-28648-6_32

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22843-1

  • Online ISBN: 978-3-540-28648-6

  • eBook Packages: Springer Book Archive

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