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International Conference on Intelligent Computing

ICIC 2018: Intelligent Computing Methodologies pp 149–162Cite as

RBF Neural Network Adaptive Sliding Mode Control of Rotary Stewart Platform

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10956))

Abstract

The stewart platform is widely applied in the industry. However, Rotary Stewart Platform (RSP) has very little research for this type. Moreover, Inverse Kinematic (IK) solution in papers published previously is complex and unclear. Therefore, in this paper, first, we design and build the mathematical model and check it in Simmechnics. Second, the robust control of the RSP proposed in this paper precisely tracks a command under the platform uncertainties. The inverse kinematic solution of the platform, derived in this paper, supports for control design of the platform. Radial Basis Function (RBF) neural network adaptive sliding mode controller is used to achieve the satisfactory tracking performance and the system stability. Stability of the system is guaranteed through Lyapunov theory. The simulation is conducted to illustrate the effectiveness of the proposed control for the RSP.

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Author information

Authors and Affiliations

  1. Department of Mechanical and Aerospace Engineering, Ulsan University, Ulsan, Korea

    Tan Van Nguyen

  2. Department of Mechanical Engineering, University of Ulsan, Ulsan, Korea

    Cheolkeun Ha

Authors
  1. Tan Van Nguyen
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  2. Cheolkeun Ha
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Corresponding author

Correspondence to Cheolkeun Ha .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Indian Institute of Technology Madras, Chennai, India

    M. Michael Gromiha

  3. Inha University, Incheon, Korea (Republic of)

    Kyungsook Han

  4. Liverpool John Moores University, Liverpool, United Kingdom

    Abir Hussain

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Van Nguyen, T., Ha, C. (2018). RBF Neural Network Adaptive Sliding Mode Control of Rotary Stewart Platform. In: Huang, DS., Gromiha, M., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2018. Lecture Notes in Computer Science(), vol 10956. Springer, Cham. https://doi.org/10.1007/978-3-319-95957-3_17

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  • DOI: https://doi.org/10.1007/978-3-319-95957-3_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95956-6

  • Online ISBN: 978-3-319-95957-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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