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Adaptive Wavelet Neural Network Friction Compensation of Mechanical Systems

  • Shen-min Song
  • Zhuo-yi Song
  • Xing-lin Chen
  • Guangren Duan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)

Abstract

Recently, based on multi-resolution analysis, wavelet neural networks (WNN) have been proposed as an alternative to NN for approximating arbitrary nonlinear functions in L 2(R). Discontinuous friction function is an unavoidable nonlinear effect that can limit control performance in mechanical systems. In this paper, adaptive WNN is used to design a friction compensator for a single joint mechanical system. Then asymptotically stability of the system is assured by adding a PD controller and adaptive robust terms. The simulation results show the validity of the control scheme.

Keywords

Tracking Error Wavelet Neural Network Neural Network Control Friction Compensation Friction Function 
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.
    Hagan, M.T., Demuth, H.B., de Jesus, O.: An Introduction to the Use of Neural Networks in Control Systems, pp. 202–248. Taylor and Francis, Philadelphia (1999)Google Scholar
  2. 2.
    Chen, F.C., Khalil, H.K.: Adaptive Control of Nonlinear Systems Using Neural Networks. Int. J. Contr. 55(6), 1299–1317 (1992)MATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Kim, Y.H., Lewis, F.L.: Reinforcement Adaptive Learning Neural Network Based Friction Compensation for High Speed and Precision. In: Proc. 37th IEEE Conf. Decision Contr. Tampa FL, pp. 2231–2238 (1998)Google Scholar
  4. 4.
    Qiang, S., Gao, X.Z., Zhuang, X.: Neural Networks in Friction Compensation, Velocity and Acceleration Measurement and PID Design. In: IEEE ICIT 2002, Bangkok, Thailand, pp. 1145–1152 (2002)Google Scholar
  5. 5.
    Selmic, R.R., Lewis, F.L.: Neural-Network Approximation of Piecewise Continuous Functions: Application to Friction Compensation. IEEE transactions on Neural Networks 13(3), 745–751 (2002)CrossRefGoogle Scholar
  6. 6.
    Lewis, F.L., Jagannathan, S., Yesilidrek, A.: Neural Network Control of Robot Manipulators and Nonlinear Systems, pp. 10–18. Taylor and Francis, Philadelphia (1999)Google Scholar
  7. 7.
    Lewis, F.L.: Neural Network Control of Robot Manipulators. IEEE Expert Special Track Intell. Contr. 64(1), 64–75 (1996)Google Scholar
  8. 8.
    Armstrong-Hélouvry, B., Dupont, P., de Wit, C.C.: A Survey of Models, Analysis Tools and Compensation Methods for the Control of Machines with Friction. Automatica 30(7), 1083–1138 (1994)MATHCrossRefGoogle Scholar
  9. 9.
    Zhang, Q., Benveniste, A.: Wavelet Networks. IEEE Trans. Neural Networks 3, 889–898 (1992)CrossRefGoogle Scholar
  10. 10.
    Pati, Y.C., Krishnaprasad, P.S.: Analysis and Synthesis of Feed-forward Neural Networks Using Discrete Affine Wavelet Transformations. IEEE Trans. Neural Networks 3(4), 73–85 (1993)CrossRefGoogle Scholar
  11. 11.
    Zhang, J., Walter, G.G., Miao, Y., Lee, W.N.W.: Wavelet Neural Networks for Function Learning. IEEE Trans. Signal Processing 43(4), 1485–1497 (1995)CrossRefGoogle Scholar
  12. 12.
    Mallat, S.G.: A Theory for Multi-resolution Signal Decomposition: the Wavelet Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 11(7), 674–693 (1989)MATHCrossRefGoogle Scholar
  13. 13.
    Gao, X.: A Comparative Research on Wavelet Neural Networks. In: Proceedings of the 9th International Conference on Neural Information Processing, pp. 699–703 (2002)Google Scholar
  14. 14.
    Armstrong, B.: Friction: Experimental Determination, Modeling and Compensation. In: Proc. IEEE Int. Conf. Robot. Automat., Philadelphia, PA, pp. 1422–1427 (1988)Google Scholar
  15. 15.
    Delyon, B., Juditsky, A., Benveniste, A.: Accuracy Analysis for Wavelet Approximations. IEEE Trans. Neural Networks 6(2), 332–348 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Shen-min Song
    • 1
  • Zhuo-yi Song
    • 1
  • Xing-lin Chen
    • 1
  • Guangren Duan
    • 1
  1. 1.School of Astronautics, Department of Control Science and EngineeringHarbin Institute of TechnologyHarbinChina

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