Use of Adaptive Learning Radial Basis Function Network in Real-Time Motion Tracking of a Robot Manipulator (original) (raw)

Abstract

In this paper, real time motion tracking of a robot manipulator based on the adaptive learning radial basis function network is proposed. This method for adaptive learning needs little knowledge of the plant in the design processes. So the centers and widths of the employed radial basis function network (RBFN) as well as the weights are determined adaptively. With the help of the RBFN, motion tracking of the robot manipulator is implemented without knowing the information of the system in advance. Furthermore, identification error and the tuned parameters of the RBFN are guaranteed to be uniformly ultimately bounded in the sense of Lyapunov’s stability criterion.

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Authors and Affiliations

  1. Department of Electrical Engineering, Korea University, 1, 5-Ka Anam-Dong, Seongbuk-Gu, Seoul, 136-701, Korea
    Dongwon Kim, Sung-Hoe Huh & Gwi-Tae Park
  2. Department of Electrical & Electronic Engineering, Anyang University, 708-113, Anyang 5dong, Manan-gu, Anyang-shi, Kyunggi-do, 430-714, Korea
    Sam-Jun Seo

Authors

  1. Dongwon Kim
  2. Sung-Hoe Huh
  3. Sam-Jun Seo
  4. Gwi-Tae Park

Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore
    Lipo Wang
  2. School of Software, Sun Yat-Sen University, 510275, Guangzhou, China
    Ke Chen
  3. School of Computer Engineering, Nanyang Technological University, BLK N4, 2b-39, Nanyang Avenue, 639798, Singapore
    Yew Soon Ong

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© 2005 Springer-Verlag Berlin Heidelberg

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Kim, D., Huh, SH., Seo, SJ., Park, GT. (2005). Use of Adaptive Learning Radial Basis Function Network in Real-Time Motion Tracking of a Robot Manipulator. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539902\_139

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