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Tip Tracking of a Flexible-Link Manipulator with Radial Basis Function and Fuzzy System

  • Yuangang Tang
  • Fuchun Sun
  • Zengqi Sun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3498)

Abstract

With output redefinition of flexible-link manipulators an adaptive controller for tip tracking is presented based on radial basis function network (RBFN) and fuzzy system. The uniformly asymptotical stability (UAS) of control system is guaranteed by the Lyapunov analysis and the adaptive laws including the centers and widths of Gaussian functions and coefficients of Takagi-Sugeno (TS) consequences can make the tracking error converge to zero. For comparison purpose, an RBFN controller with fixed centers and widths is also designed. The simulation results show that with similar performances the proposed controller can give smoother input torques than the conventional RBFN one.

Keywords

Fuzzy System Radial Basis Function Network Flexible Manipulator Flexible Link Uniformly Asymptotical Stability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Poggio, T., Girosi, F.: Networks for Approximation and Learning. Proc. of the IEEE 78, 1481–1497 (1990)CrossRefGoogle Scholar
  2. 2.
    Ying, H.: General Takagi-Sugeno Fuzzy Systems are Universal Approximators. In: Proc. of the IEEE International Conference on Fuzzy Systems, vol. 1, pp. 819–823 (1998)Google Scholar
  3. 3.
    Tso, S.K., Lin, N.L.: Adaptive Control of Robot Manipulator with Radial-basis-function Neural Network. In: IEEE International Conference on Neural Networks, vol. 3, pp. 1807–1811 (1996)Google Scholar
  4. 4.
    Lee, M.J., Choi, Y.K.: An Adaptive Neurocontroller Using RBFN for Robot Manipulators. IEEE Trans. on Industrial Electronics 51, 711–717 (2004)CrossRefGoogle Scholar
  5. 5.
    Arciniegas, J.I., Eltimsahy, A.H., Cios, K.J.: Neural-networks-based Adaptive Control of Flexible Robotic Arms. Neurocomputing 17, 141–157 (1997)CrossRefGoogle Scholar
  6. 6.
    Mannani, A., Talebi, H.A.: TS-model-based Fuzzy Tracking Control for a Single-link Flexible Manipulator. In: Proc. of IEEE Conference on Control Applications, vol. 1, pp. 374–379 (2003)Google Scholar
  7. 7.
    Anderson, H.C., Lotfi, A., Westphal, L.C., Jang, J.R.: Comments on Functional Equivalence Between Radial Basis Function Networks and Fuzzy Inference Systems [and reply]. IEEE Trans. on Neural Networks 9, 1529–1532 (1998)CrossRefGoogle Scholar
  8. 8.
    De Luca, A., Siciliano, B.: Closed-form Dynamic Model of Planar Multilink Lightweight Robots. IEEE Trans. on Systems, Man and Cybernetics 21, 826–839 (1991)CrossRefGoogle Scholar
  9. 9.
    De Luca, A., Siciliano, B.: Regulation of Flexible Arms Under Gravity. IEEE Trans. on Robotics and Automation 9, 463–467 (1993)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Yuangang Tang
    • 1
  • Fuchun Sun
    • 1
  • Zengqi Sun
    • 1
  1. 1.State Key Lab of Intelligent Technology and Systems, Department of Computer Science and TechnologyTsinghua UniversityBeijingChina

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