Robust Control of Planar Biped Robots in Single Support Phase Using Intelligent Adaptive Backstepping Technique

  • Yoo, Sung-Jin (Department of Electrical and Electronic Engineering, Yonsei University) ;
  • Park, Jin-Rae (Department of Electrical and Electronic Engineering, Yonsei University) ;
  • Choi, Yoon-Ho (School of Electronic Engineering, Kyonggi University)
  • Published : 2007.06.30

Abstract

This paper presents a robust control method via the intelligent adaptive backstepping design technique for stable walking of nine-link biped robots with unknown model uncertainties and external disturbances. In our control structure, the self recurrent wavelet neural network(SRWNN) which has the information storage ability is used to observe the uncertainties of the biped robots. The adaptation laws for all weights of the SRWNN are induced from the Lyapunov stability theorem, which are used for on-line controlling biped robots. Also, we prove that all signals in the closed-loop adaptive system are uniformly ultimately bounded. Through computer simulations of a nine-link biped robot with model uncertainties and external disturbances, we illustrate the effectiveness of the proposed control system.

Keywords

References

  1. H. K. Lum, M. Zribi, and Y. C. Soh, 'Planning and control of a biped robot,' Int. Jour. Engineering Science, vol. 37, no. 2, pp. 1319-1349, 1999 https://doi.org/10.1016/S0020-7225(98)00118-9
  2. 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
  3. S. G. Tzafestas, T. E. Krikochoritis, and C. S.Tzafestas, 'Robust sliding-mode control of ninelink biped robot walking,' Jour. of Intelligent and Robotic Systems, vol. 20, pp. 375-402, 1997 https://doi.org/10.1023/A:1007924731253
  4. 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
  5. S. G. Tzafestas, A. E. Krikochoritis, and C. S. Tzafestas, 'Robust-adaptive gait control of a 9- link biped robot', Systems Analysis Modelling Simulation, vol. 31, pp. 247-304, 1998
  6. K. Mitobe, N. Mori, and Y. Nasu, 'Control of a biped walking robot during the double support phase,' Autonomous Robots, vol. 4, pp. 287-296, 1997 https://doi.org/10.1023/A:1008896010368
  7. 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
  8. Z. Liu, C. Li, and W. Xu, 'Hybrid control of biped robots in the double-support phase via H$\infty$ approach and fuzzy neural networks,' IEE Proc. Control Theory and Applications, vol. 150, no. 4, pp. 347-354, 2003
  9. Z. Liu, C. Li, and W. Xu, 'Fuzzy neural network quadratic stabilization output feedback control for biped robots via H$\infty$ approach,' IEEE Trans. on Systems, Man, and Cybernetics-Part B: Cybernetics, vol. 33, no.1, pp. 67-84, 2003
  10. Q. Zhang and A. Benveniste, 'Wavelet networks,' IEEE Trans. on Neural Networks, vol. 3, no. 6, pp. 889-898, 1992 https://doi.org/10.1109/72.165591
  11. J. Zhang, G. Walter, Y. Miao, and W. N. W. Lee, 'Wavelet neural networks for function learning,' IEEE Trans. on Signal Processing, vol. 43, no. 6, pp. 1485-1497, 1995 https://doi.org/10.1109/78.388860
  12. Y. Oussar, I. Rivals, L. Personnaz, and G. Dreyfus, 'Training wavelet networks for nonlinear dynamic input-output modeling,' Neurocomputing, vol. 20, no. 1-3, pp. 173-188, 1998 https://doi.org/10.1016/S0925-2312(98)00010-1
  13. S. J. Yoo, J. B. Park, and Y. H. Choi, 'Stable predictive control of chaotic systems using selfrecurrent wavelet neural network,' Int. Jour. of Control, Automation, and Systems, vol. 3, no. 1, pp. 43-55, 2005
  14. S. J. Yoo, Y. H. Choi, and J. B. Park, 'Generalized predictive control based on self recurrent wavelet neural network for stable path tracking of mobile robots: Adaptive learning rates approach,' IEEE Trans. on Circuits Syst. I, Reg. Papers, vol. 53, no. 6, pp. 1381-1394, Jun. 2006 https://doi.org/10.1109/TCSI.2006.875166
  15. H. Wang, T. T. Lee, W. A. Gruver, 'A neuromorpic controller for a three-link biped robot,' IEEE Trans. on Systems, Man, and Cybernetics-Part B: Cybernetics, vol. 22, no. 1, pp. 164-169, 1992 https://doi.org/10.1109/21.141321
  16. M. Krstic, I. Kanellakopoulos, and P. Kokotovic, Nonlinear and Adaptive Control Design, Wiley Interscience, NJ, 1995
  17. C. Kwan and F. L. Lewis, 'Robust backstepping control of nonlinear systems using neural networks,' IEEE Trans. on Systems, Man, and Cybernetics, vol. 30, no. 6, pp. 753-766, 2000 https://doi.org/10.1109/3468.895898
  18. E. Kim, 'Output feedback tracking control of robot manipulators with model uncertainty via adaptive fuzzy logic,' IEEE Trans. on Fuzzy Systems, vol. 12, no 3, pp.368-378, 2004 https://doi.org/10.1109/TFUZZ.2004.825062
  19. 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. on Industrial Electronics, vol. 50, no. 3, pp. 456-470, 2003 https://doi.org/10.1109/TIE.2003.809394
  20. C. Y. Lee and J. J. Lee, 'Multiple neuro-adaptive control of robot manipulators using visual cues,' IEEE Trans. on Industial Electronics, vol. 52, no 1, pp. 320-326, 2005 https://doi.org/10.1109/TIE.2004.841080
  21. L. X. Wang, A Course in Fuzzy Systems and Contro, Prentice-Hall, Upper Saddle River, NJ, 1997
  22. K. S. Narendra and A. M. Annaswamy, 'A new adaptive law for robust adaptation without persistent excitation,' IEEE Trans. on Automatic Control, vol. 32, no. 2, pp. 134-145, 1987 https://doi.org/10.1109/TAC.1987.1104543