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A Local Neural Networks Approximation Control of Uncertain Robot Manipulators

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Advanced Intelligent Computing Theories and Applications (ICIC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9227))

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Abstract

In this paper, an adaptive finite-time tracking control scheme is proposed for uncertain robotic manipulators. The controller is developed based on combination of terminal sliding mode control technique and radian basis function neural networks (RBFNNs). The RBFNNs are used to directly approximate individual element of the inertial matrix, the Coriolis matrix and gravity torques vector. The adaptation laws are derived to adjust on-line the parameters of RBFNNs. Finally, the simulation results of a two-link robot manipulator are presented to illustrate the effectiveness of the proposed control method.

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Acknowledgments

This work was supported by the Research Fund of University of Ulsan.

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Authors and Affiliations

  1. Graduate School of Electrical Engineering, University of Ulsan, Ulsan, 680-749, South Korea

    Minh-Duc Tran

  2. School of Electrical Engineering, University of Ulsan, Ulsan, 680-749, South Korea

    Hee-Jun Kang

Authors
  1. Minh-Duc Tran
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  2. Hee-Jun Kang
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Corresponding author

Correspondence to Hee-Jun Kang .

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Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Incheon, Korea (Republic of)

    Kyungsook Han

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Tran, MD., Kang, HJ. (2015). A Local Neural Networks Approximation Control of Uncertain Robot Manipulators. In: Huang, DS., Han, K. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2015. Lecture Notes in Computer Science(), vol 9227. Springer, Cham. https://doi.org/10.1007/978-3-319-22053-6_58

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  • DOI: https://doi.org/10.1007/978-3-319-22053-6_58

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22052-9

  • Online ISBN: 978-3-319-22053-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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