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Dynamic Tracking Control of Mobile Robots Using an Improved Radial Basis Function Neural Network

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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

A novel dynamic control scheme for nonholonomic mobile robots is developed in this paper. The dynamics of mobile robot based on improved radial basis function neural network (IRBFNN) is modeled online by the improved algorithm of resource allocating network (IRAN). The control scheme of mobile robot integrates a velocity controller based on backstepping technology and a torque controller based on the IRBFNN and robust compen-sator. The simulations have shown that the control system is competent for the robust tracking control of mobile robot.

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Author information

Authors and Affiliations

  1. College of Automation, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China

    Shirong Liu & Qijiang Yu

  2. Research Institute of Automation, East China University of Science and Technology, Shanghai, 200237, China

    Qijiang Yu & Jinshou Yu

Authors
  1. Shirong Liu
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  2. Qijiang Yu
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  3. Jinshou Yu
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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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Liu, S., Yu, Q., Yu, J. (2006). Dynamic Tracking Control of Mobile Robots Using an Improved Radial Basis Function Neural Network. 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_172

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  • DOI: https://doi.org/10.1007/11760023_172

  • 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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