Hierarchical ANFIS Identification of Magneto-Rheological Dampers

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Abstract:

Magneto-rheological (MR) dampers, recently, have been widely utilized in many different areas of engineering for their high properties. There are two different kinds of problems for MR dampers, the direct model and the inverse one. It is difficult to express of the direct model of the MR damper for its high nonlinearity and hysteretic characteristics. It is much more difficult to get the inverse model of MR damper, which means the determination of the input voltage so as to gain the desired restoring force decided by the control law. When identifying the direct and the inverse model of MR damper with Adaptive Neuro-Fuzzy Inference System (ANFIS), there exists curse of dimensionality of fuzzy system. Therefore, it will take much more time, and even the inverse model may not be identifiable. The paper presents a hierarchical ANFIS to deal with the curse of dimensionality of the fuzzy identification of MR damper and to identify the direct and the inverse model of MR damper. The numerical simulation proves that the proposed hierarchical ANFIS can model the direct and the inverse model of MR damper much more quickly than ANFIS without more changing of identification precision. Such hierarchical ANFIS shows the higher priority for the complicated system, and can also be used in system identification and system control for the complicated system.

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343-348

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August 2010

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[1] M. R. Jolly, J. W. Vender and J. D. Carlson, Properties and application of commercial Magneto-rheological fluids, Journal of Intelligent Material Systems and Structures, 10(1), 5-13 (1999).

Google Scholar

[2] B. F. Spencer, S. J. Dyke, M. K. Sain, et al. Phenomenological model of a magneto-rheological damper, Journal of Engineering Mechanics, ASME, 123(3), 230-38 (1997).

Google Scholar

[3] D. L. Guo, H. Y. Hu and J. Q. Yi, �eutral network control for a semi-active vehicle suspension with a Magneto-rheological Damper, Journal of Vibration and Control. 10(3), 461-471 (2004).

DOI: 10.1177/1077546304038968

Google Scholar

[4] S. J. Dyke and B. F. Spencer, A comparison of semi-active control strategies for the MR damper, Intelligent Information Systems. 8-10: 580-584 (1997).

DOI: 10.1109/iis.1997.645424

Google Scholar

[5] P. Q. Xia, An inverse model of MR damper using optimal neural network and system identification, Journal of Sound and Vibration. 266(5), 1009-1023 (2003).

DOI: 10.1016/s0022-460x(02)01408-6

Google Scholar

[6] C. C. Chang and L. Zhou, �eural network emulation of inverse dynamics for a magneto-rheological damper, Journal of Structure Engineering. ASCE, 128(2), 231-239 (2002).

Google Scholar

[7] K. C. Schurter and P. N. Roschke, Fuzzy Modeling of A Magneto-rheological Damper Using A�FIS, Fuzzy Systems. FUZZ IEEE 2000. The Ninth IEEE International Conference, San Antonio, TX, USA, 1, 122-127 (2000).

DOI: 10.1109/fuzzy.2000.838645

Google Scholar

[8] H. Wang and H. Y. Hu, The fuzzy approximation of MR damper, Journal of Vibration Engineering. 19(1), 31-36 (in Chinese)(2006).

Google Scholar

[9] L. X. Wang, Analysis and Design of Hierarchical Fuzzy Systems, Fuzzy Systems, IEEE Transactions on Volume 7, Issue 5, Oct. 7(5), 617-24 (1999).

Google Scholar

[10] J. S. R. Jang, A�FIS: Adaptive-�etwork-Based Fuzzy Inference Systems, IEEE Trans. On Systems, Man, and Cybernetics. 23(3), 665-85 (1993).

DOI: 10.1109/21.256541

Google Scholar

[11] J. Stufflebeam and N.R. Prasad, Hierarchical Fuzzy Control, " Fuzzy Systems Conference Proceedings, 1999. FUZZY-IEEE , 99. IEEE International Volume 1(1), 498-503 (1999).

DOI: 10.1109/fuzzy.1999.793291

Google Scholar