Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Adaptive Backstepping Control Using Self Recurrent Wavelet Neural Network for Stable Walking of the Biped Robots

이족 로봇의 안정한 걸음새를 위한 자기 회귀 웨이블릿 신경 회로망을 이용한 적응 백스테핑 제어

  • 유성진 (연세대학교 전기전자공학과) ;
  • 박진배 (연세대학교 전기전자공학과)
  • Published : 2006.03.01
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Abstract

This paper presents the robust control method using a self recurrent wavelet neural network (SRWNN) via adaptive backstepping design technique for stable walking of biped robots with unknown model uncertainties. The SRWNN, which has the properties such as fast convergence and simple structure, is used as the uncertainty observer of the biped robots. The adaptation laws for weights of the SRWNN and reconstruction error compensator are induced from the Lyapunov stability theorem, which are used for on-line controlling biped robots. Computer simulations of a five-link biped robot with unknown model uncertainties verify the validity of the proposed control system.

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References

  1. S. Tzafestas, M. Raibert, and C. Tzafestas, 'Robust sliding-mode control applied to a 5-link biped robot,' Jour. of Intelligent and Robotic Systems, vol. 15, pp. 67-133, 1996 https://doi.org/10.1007/BF00435728
  2. H. K. Lum, M. Zribi, and Y. C. Soh, 'Planning and control of a biped robot,' Int. Jour. of Engineering Science, vol 37, no. 10, pp. 1319-1349, 1999 https://doi.org/10.1016/S0020-7225(98)00118-9
  3. L. C. Kwek, E. K. Wong, C. K. Loo, and M. V. C. Rao, 'Application of active force control and iterative learning in a 5-link biped robot,' Jour. of Intelligent and Robotic systems, vol. 37, pp. 143-162, 2003 https://doi.org/10.1023/A:1024187206507
  4. M. Krstic, J. Kanellakopoulos, and P. Kokotovic, Nonlinear and adaptive control design, NJ: Wiley Interscience, 1995
  5. H. K. Khalil, Nonlinear Systems, Upper Saddle River, NJ: Prentice-Hall, 1996
  6. S. J. Yoo, J. B. Park, and Y. H. Choi, 'Stable predictive control of chaotic systems using self-recurrent wavelet neural network,' Int. Jour. of Control, Automation, and Systems, vol. 3, no. 1, pp. 43-55, 2005
  7. S. J. Yoo, J. B. Park, and Y. H. Choi, 'Direct adaptive control using self recurrent wavelet neural network via adaptive learning rates for stable path tracking of mobile robots,' Proc. of American Control Conference, pp. 288-293, 2005 https://doi.org/10.1109/ACC.2005.1469947
  8. Q. Zhang and A. Benveniste, 'Wavelet networks,' IEEE Trans. Neural Networks, vol. 3, no. 6, pp. 889-898, 1992 https://doi.org/10.1109/72.165591
  9. F. J. Lin, T. S. Lee, and C. H. Lin, 'Robust $H_{\infty}$ controller design with recurrent neural network for linear synchronous motor drive,' IEEE Trans. Industrial Electronics, vol. 50, no. 3, pp. 456-470, 2003 https://doi.org/10.1109/TIE.2003.809394
  10. E. Kim, 'Output feedback tracking control of robot manipulators with model uncertainty via adaptive fuzzy logic,' IEEE Trans. Fuzzy Systems, vol. 12, no 3, pp. 368-378, 2004 https://doi.org/10.1109/TFUZZ.2004.825062
  11. X. Mu, and Q. Wu, 'Development of a complete dynamic model of a planar five-link biped and sliding mode control of its locomotion during the double support phase,' Int. Jour. of Control, vol. 77, no. 8, pp. 789-799, 2004 https://doi.org/10.1080/00207170410001705005
  12. F. L. Lewis, C. T. Abdallah, and D. M. Dawson, Control of Robot Manipulators. New York: Macmillan, 1993
  13. C. Y. Lee, and J. J. Lee, 'Multiple neuro-adaptive control of robot manipulators using visual cues,' IEEE Trans. Industrial Electronics, vol. 52, no. 1, pp. 320-326, 2005 https://doi.org/10.1109/TIE.2004.841080