International Journal of Fuzzy Logic and Intelligent Systems

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Inverse Model Control of An ER Damper System

  • Cho Jeong-Mok (Dept. of Control & Instrumentation Eng. Changwon National University) ;
  • Jung Taeg-Eun (Dept. of Control & Instrumentation Eng. Changwon National University) ;
  • Kim Dong-Hyeon (Agency for Defense Development) ;
  • Joh Joong-Seon (Dept. of Control & Instrumentation Eng. Changwon National University)
  • Published : 2006.03.01
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Abstract

Due to the inherent nonlinear nature of Electro-rheological (ER) fluid dampers, one of the challenging aspects for utilizing these devices to achieve high system performance is the development of accurate models and control algorithms that can take advantage of their unique characteristics. In this paper, the nonlinear damping force model is made to identify the properties of the ER damper using higher order spectrum. The higher order spectral analysis is used to investigate the nonlinear frequency coupling phenomena with the damping force signal according to the sinusoidal excitation of the damper. Also, this paper presents an inverse model of the ER damper, i.e., the model can predict the required voltage so that the ER damper can produce the desired force for the requirement of vibration control of vehicle suspension systems. The inverse model is constructed by using a multi-layer perceptron neural network. A quarter-car suspension model is considered in this paper for analysis and simulation. Simulation results show that the proposed inverse model of ER damper can obtain control voltage of ER damper for required damping force.

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References

  1. W. H. Winslow, 'Induced fibration of suspension', Journal of Applied Physics 20, pp. 1137-1140, 1949 https://doi.org/10.1063/1.1698285
  2. R. Stanway, J. Sproston, and R. Firoozian, 'Identification of the damping law of an Electro-Rheological Fluid', ASME J. of Dynamic Systems, Measurement and Control, Vol. 111, pp. 91-96, 1989 https://doi.org/10.1115/1.3153023
  3. G. Mui, D. L. Russel and J. Y. Wong, 'Non-linear Parameter Identification of an Electro-Rheological fluid Damper', Proceedings of the 5th International Conference on ER Fluids, Sheffiedl, U. K., pp. 690-697, 1995
  4. D. R. Gamota and F. E. Filisko, 'Dynamic Mechanical Studies of Electrorheological Materials: Moderate Frequencies', J. of Rheol. 35(3), pp. 399-425, 1991 https://doi.org/10.1122/1.550221
  5. J. A. Powell, 'Modelling the oscillatory response of an electrorheological fluid', Smart Materials Struct. 3, pp. 416-438, 1994 https://doi.org/10.1088/0964-1726/3/4/005
  6. S. B. Choi, S. K. Lee, and Y. P. Park, 'A hysteresis model for the field-dependent damping force of a magneto-rheological damper', Journal of Sound and Vibration 245(2), pp. 375-383, 2001 https://doi.org/10.1006/jsvi.2000.3539
  7. P. Q. Xia, 'An inverse model of MR damper using optimal neural network and system identification', Journal of Sound and Vibration 266, pp. 1009-1023, 2003 https://doi.org/10.1016/S0022-460X(02)01408-6
  8. D. H. Wang and W. H. Liao, 'Modeling and control of magneto-rheological fluid dampers using neural networks', Smart Materials and Structures 14, pp. 111-126, 2005 https://doi.org/10.1088/0964-1726/14/1/011
  9. C. L. Nikias and M. R. Raghuveer, 'Bisprctrum Estimation', Proceedings of IEEE, Vol. 75, No.7, pp. 869-891, 1987
  10. C. L. Nikias and J. M. Mendel, 'Signal Processing with High Order Spectra', IEEE Signal Processing Magazine, pp. 10-37, 1993
  11. K. I. Kim and E. J. Powers, 'A Digital Method of Modeling Quadratically Nonlinear Systems with a General Random Input', IEEE Transaction on Acoustics, Speech and Signal Processing, Vol. 36, No. 11, pp. 1758-1769, 1988 https://doi.org/10.1109/29.9013
  12. S. W. Nam and E. J. Powers, 'Application of High Order Spectral Analysis to Cubically Nonlinear System Identification', IEEE transaction on Signal Processing, Vol. 42, No.7, pp. 1746-1765, 1994 https://doi.org/10.1109/78.298282
  13. J. Cho, T. Jung, S. Kim, and J. Joh, 'Fuzzy Skyhook Control of Semiactive Suspensions Using GA', Smart Structures and Materials 2006