Backstepping Control-Based Precise Positioning Control Using Robust Friction State Observer and RFNN

강인한 마찰상태관측기와 RFNN을 이용한 백스테핑 제어기반 정밀 위치제어

  • Received : 2009.12.21
  • Accepted : 2010.06.03
  • Published : 2010.06.15

Abstract

In this article, we investigate a robust friction compensation scheme for the purpose of accomplishing precision positioning performance a servo mechanical system with nonlinear dynamic friction. To estimate the friction state and tackle robustness problem for uncertainty, a RFNN and reconstructed error compensator as well as a robust friction state observer are developed. The asymptotic stability of the series of friction compensation methodologies are verified from the Lyapunov's stability theory. Some simulations and experiments on a servo mechanical system were carried out to evaluate the effectiveness of the proposed control scheme.

Keywords

References

  1. Canudas de Wit, C., Olsson, H., and Astrom, K. J., 1995, "A new model for control of systems with friction," IEEE Trans A.C., Vol. 40, No. 3, pp. 419-425. https://doi.org/10.1109/9.376053
  2. Dupong, P., Hayward, V., Armstrong, B., and Alpeter, J., 2002, "Single state elasto-plastic friction models," IEEE Trans A.C., Vol. 47, No. 5, pp. 787-792. https://doi.org/10.1109/TAC.2002.1000274
  3. AI-Bender, F., Lampaert, V., and Swevers, J., 2005, "The generalized Maxwell-slip model: a novel model for friction simulation and compensation," IEEE Trans. A.C., Vol. 50, No. 11, pp. 1883-1887. https://doi.org/10.1109/TAC.2005.858676
  4. Ge, S. S., Lee, T. H., and Rcn, S. X., 2001, "Adaptive friction compensation for servo mechanism," Int. J. System Science, Vol. 32, No. 3, pp. 523-532. https://doi.org/10.1080/00207720119378
  5. Xie, W. F., 2007, "Sliding-mode-observer-based adaptive control for servo actuator with friction," IEEE Trans. Indust. Elect., Vol. 54, No. 3, 1517-1527. https://doi.org/10.1109/TIE.2007.894718
  6. Alvarez, L., Yi, J. G., Horowitz, R., and Olmos, L., 2005, "Dynamic friction model-based tire-road friction estimation and emergency braking control," Trans. ASME, Vol. 127, March, pp. 22-32.
  7. Lin, F. J., Hwang, W. J., and Wai, R. J., 1997, "A supervisory fuzzy neural network control system for tracking periodic inputs," IEEE, Trans. Fuzzy Syst., Vol. 7, No. 1, pp. 41-52.
  8. Lin, C. H., 2004, "Adaptive recurrent fuzzy neural network control for synchronous reluctance motor servo drive," lEE Proc Electr. Power Appl., Vol. 151, No. 6, pp. 711-724. https://doi.org/10.1049/ip-epa:20040687
  9. Han, S. I., 2008, "Robust control for nonlinear servo system using fuzzy neural network and robust friction state observer," KSPE, Vol. 25, No. 6, pp. 89-99.
  10. Han, S. I., 2009, "Robust adaptive Back-stepping control using dual friction observer and RNN with disturbance observer for dynamic friction model," J. of KSMTE, Vol. 18, No. 1, pp. 50-58.
  11. Han, S. I., 2009, "Nonlinear friction control using the robust friction state observer and recurrent fuzzy neural network estimator," J of KSMTE, Vol. 90, No. 1, pp. 90-102.
  12. Slotine, J. E. and Li, W., 1991, Applied Nonlinear Control, Prentice -Hall, New Jersey.