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

Yeo, Dae-Yeon (동아대학교 전기공학과)
Han, Seong-Ik (동아대학교 전기공학과)
Lee, Kwon-Soon (동아대학교 전기공학과)
Publication Information
Journal of the Korean Society of Manufacturing Technology Engineers / v.19, no.3, 2010 , pp. 394-401 More about this Journal
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
LuGre friction model; Backstepping control; Robust friction slate observer; Recurrent fuzzy neural networks; Reconstructed error compensator; Servo mechanical system;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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1 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.   과학기술학회마을
2 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.   과학기술학회마을
3 Slotine, J. E. and Li, W., 1991, Applied Nonlinear Control, Prentice -Hall, New Jersey.
4 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.
5 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.
6 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.   DOI
7 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.   DOI   ScienceOn
8 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.   과학기술학회마을
9 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.   DOI
10 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.   DOI
11 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.   DOI   ScienceOn
12 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.   DOI   ScienceOn