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Support Vector Machine Based Approach for Abstracting Human Control Strategy in Controlling Dynamically Stable Robots

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Abstract

In this paper, we discuss the problem of how human control strategy can be represented as a parametric model using a support vector machine (SVM) and how an SVM-based controller can be used in controlling dynamically stable systems. The SVM approach has been implemented in the balance control of Gyrover, which is a dynamically stable, statically unstable, single-wheel mobile robot. The experimental results that compare SVM with general artificial neural network approaches clearly demonstrate the superiority of the SVM approach with regard to human control strategy learning.

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Authors and Affiliations

  1. Department of Mechanical Engineering and Mechanics, Lehigh University, Bethlehem, PA, 18015, USA

    Yongsheng Ou

  2. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, N.T. Hong Kong SAR, China

    Huihuan Qian & Yangsheng Xu

Authors
  1. Yongsheng Ou
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  2. Huihuan Qian
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  3. Yangsheng Xu
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Correspondence to Yongsheng Ou.

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Ou, Y., Qian, H. & Xu, Y. Support Vector Machine Based Approach for Abstracting Human Control Strategy in Controlling Dynamically Stable Robots. J Intell Robot Syst 55, 39–54 (2009). https://doi.org/10.1007/s10846-008-9292-8

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  • DOI: https://doi.org/10.1007/s10846-008-9292-8

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