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A Study on the Intelligent Position Control System Using Sliding Mode and Friction Observer

슬라이딩 모드와 마찰관측기를 이용한 강인한 지능형 위치 제어시스템 연구

  • 한성익 (동아대학교 전기공학과) ;
  • 이영진 (한국폴리텍 항공대학 항공전기과) ;
  • 이권순 (동아대학교 전기공학과) ;
  • 남현도 (단국대학교 전자전기공학부)
  • Received : 2010.02.26
  • Accepted : 2010.04.28
  • Published : 2010.06.01

Abstract

A robust positioning control system has been studied using a friction parameter observer and a recurrent fuzzy neural network based on the sliding model. To estimate a nonlinear friction parameters of the LuGre friction model, a dual friction model-based observer is introduced. In addition, an approximating method for a system uncertainty has been developed using a recurrent fuzzy neural network technique to improve positioning performance. Experimental results have been presented to validate the performance of a proposed intelligent compensation scheme.

Keywords

References

  1. Dahl, P., "Solid friction damping of mechanical vibrations," AIAA J., Vol 12, pp. 1675-1682, 1976.
  2. Canudas de Wit, C., Olsson, H., and Astrom, K. J., "A new model for control of systems with friction," IEEE Trans. Autom. Control, Vol. 40(3), pp. 419-425, 1995. https://doi.org/10.1109/9.376053
  3. Dupong, P., Hayward, V., Armstrong, B., and Altpeter, J., "Single state elasto-plastic friction models," IEEE Trans. Autom. Control, Vol. 47(5), pp. 787-792, 2002. https://doi.org/10.1109/TAC.2002.1000274
  4. Swevers, J., Al-Bender, F., Ganseman, C., and Prajogo, T., "An integrated friction model structure with improved presliding behavior for accurate friction structure," IEEE Trans. Automatic Control, Vol. 45(4), pp. 675-686, 2000. https://doi.org/10.1109/9.847103
  5. Choi, J. J., Han, S. I., and Kim, J. S., "Development of a novel dynamic friction model and precise tracking control using adaptive back-stepping sliding mode controller," Mechatonics, Vol. 16, pp. 87-104, 2006.
  6. Al-Bender, F., Lampaert, V., and Swever, J., "The generalized Maxwell-slip model: a novel model for friction simulation and compensation," IEEE Trans. Autom. Control, Vol. 50(11), pp. 1883-1887, 2005. https://doi.org/10.1109/TAC.2005.858676
  7. Canudas de Wit, C. and Lischinsky, P., "Adaptive friction compensation with partially known dynamic friction model," Int. J. Adapt. Control Signal Process., Vol. 11, pp. 65-80, 1997. https://doi.org/10.1002/(SICI)1099-1115(199702)11:1<65::AID-ACS395>3.0.CO;2-3
  8. Lischinsky, P., Canudas de Wit, C., and Morel, G., "Friction compensation for an industrial hydraulic robot," Control Sys. Magazine IEEE, Vol. 19(1), pp. 25–32, 1999. https://doi.org/10.1109/37.745763
  9. Ge, S. S., Lee, T. H., and Ren, S. X., "Adaptive friction compensation of servo mechanisms," Int. J. Syst. Sci., Vol. 32(4), pp. 523-532, 2001. https://doi.org/10.1080/00207720119378
  10. Tan, Y., Chang, J., and Tan, T., "Adaptive backstepping control and friction compensation for AC servo with inertia and load uncertainties," IEEE Trans. Ind. Electron., Vol. 50(5), pp. 944-952, 2003. https://doi.org/10.1109/TIE.2003.817574
  11. Alvarez, L., Yi, J. G., Horowitz, R., and Olmos, L., "Dynamic friction model-based tire-road friction estimation and emergency braking control," J. Dynamic System Measurement, Control, Vol. 127(3), pp. 22-32, 2005. https://doi.org/10.1115/1.1870036
  12. Li, L., Wang, F. Y., and Zhou, Q. Z., "Integrated longitudinal and lateral tire/road friction modeling and monitoring for vehicle motion control," IEEE Trans. Intell. Transp. Syst., Vol. 7(1), pp. 1-17, 2006. https://doi.org/10.1109/TITS.2005.858624
  13. Huang, S. N., Tan, K. K., and Lee, T. H., "Adaptive friction compensation using neural network approximation," IEEE Trans. Syst. Man Cybern., Vol. 30(4), pp. 551-557, 2000. https://doi.org/10.1109/5326.897081
  14. Selmic, R. and Lewis, F. L., "Neural network approximation of piecewise continuous functions: application to friction compensation," IEEE Trans. Neural Netw., Vol. 50(5), pp. 745-751, 2002.
  15. Ha, Q. P., Bonchis, A., Rye, D. C., and Durrent-Whyte, H. F., "Variable structure systems approach to friction estimation and compensation," In Proceedings of the IEEE International Conference on Robotics and automation, , vol. 4, pp. 3543-3548, 2000.
  16. Lin, C. T. and Lee, C. S. G., Neural systems: a neural-fuzzy synergism to intelligent systems, 1996 (Prenctice-Hall).
  17. Leu, Y. G., Lee, T. T., and Wang, W. Y., "On-line tuning of fuzzy-neural networks for adaptive control of nonlinear dynamic systems," IEEE Trans. System Man Cybernetics., Vol. 27(6), pp. 1034-1043, 1997. https://doi.org/10.1109/3477.650065
  18. Lin, F. J., Hwang, W. J., and Wai, R. J., "A supervisory fuzzy neural network control system for tracking periodic inputs," IEEE Trans. Fuzzy System, Vol. 7(1), pp. 41-52, 1997.
  19. Wai, R. J. and Lin, F. J., "Fuzzy neural network sliding-mode position controller for induction servo motor drive," IEE Proc. B, Electr. Power Appl., Vol. 146(3), pp. 297-308, 1999. https://doi.org/10.1049/ip-epa:19990290
  20. Lin, F. J. and Wai, R. J., "Robust recurrent fuzzy neural network control for linear synchronous motor drive system," Neurocomputing, Vol. 50, pp. 365-390, 2003. https://doi.org/10.1016/S0925-2312(02)00572-6
  21. Lin, C. H., "Adaptive recurrent fuzzy neural network control for synchronous reluctance motor servo drive," IEE Proc. B, Electr. Power Appl., Vol. 151(6), pp. 711-724, 2004. https://doi.org/10.1049/ip-epa:20040687
  22. Peng, J. Z., Wang, Y. N., and Sun, W., "Trajectory tracking control for mobile robot using recurrent fuzzy cerebellar model articulation controller," Neural Inform. Process.-Lett. Rev., Vol. 11(1), pp. 15-23, 2007.
  23. Slotine, J. E. and Li, W., Applied nonlinear control, 1991 (Prentice-Hall, New Jersey).