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Nonlinear and Hysteretic Modelling of Magnetorheological Elastomer Base Isolator Using Adaptive Neuro-Fuzzy Inference System

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Abstract:

Magnetorheological elastomer (MRE) base isolator is a semi-active control device which has currently obtained increasing attention in the field of vibration control of civil structures. However, the inherent nonlinear and hysteretic response of the device is regarded as a challenge aspect for using the smart device to realize the high performance. Therefore, an accurate and robust model is essential to make full use of these unique features for its engineering applications. In this paper, to solve this issue, adaptive neuro-fuzzy inference system (ANFIS) is utilized to characterize the dynamic behavior of the device. In this proposed model, the inputs are historical displacements and applied current of the device while the output is the shear force generated. To validate its forecast performance, the ANFIS model is also compared with some conventional models. Finally, the result verifies that ANFIS has the best perfection ability among existing MRE-based device models.

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Periodical:

Applied Mechanics and Materials (Volume 846)

Pages:

258-263

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.846.258

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Online since:

July 2016

Authors:

Yang Yu*, Yan Cheng Li, Jian Chun Li, Xiao Yu Gu, Sayed Royel, Arati Pokhrel

Keywords:

Adaptive Neuro-Fuzzy Inference System (ANFIS), Hysteresis Behaviour, Magnetorheological Elastomer (MRE) Base Isolator, Nonparametric Modelling

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© 2016 Trans Tech Publications Ltd. All Rights Reserved

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* - Corresponding Author

References

[1] K.M. Popp, M. Kroger, W.H. Li, X.Z. Zhang, P.B. Kosasih, MRE properties under shear and squeeze modes and applications.

Google Scholar

[2] Y. Yu, Y. Li, J. Li, Parameter identification of an improved Dahl model for magnetorheological elastomer base isolator based on enhanced genetic algorithm, In: Proceedings of the 23rd Australasian Conference on the Mechanics of Structures and Materials. Byron Bay, Australia; 2014, pp.931-936.

Google Scholar

[3] J. Yang, H. Du, W. Li, Y. Li, J. Li, S. Sun, H. Deng, Experimental study and modeling of a novel magnetorheological elastomer isolator, Smart Mater. Struct. 22 (2013) 117001.

DOI: 10.1088/0964-1726/22/11/117001

Google Scholar

[4] Y. Yu, Y. Li, J. Li, A new hysteretic model for magnetorheological elastomer base isolator and parameter identification based on modified artificial fish swarm algorithm, In: Proceedings of 31st International Symposium on Automation and Robotics in Construction and Mining. Sydney, Australia; 2014, pp.176-183.

DOI: 10.22260/isarc2014/0024

Google Scholar

[5] Y. Yu, Y. Li, J. Li, Parameter identification and sensitivity analysis of an improved LuGre friction model for magnetorheological elastomer base isolator, Meccanica doi: 10. 1007/s11012-015-0179-z.

DOI: 10.1007/s11012-015-0179-z

Google Scholar

[6] Y. Li, J. Li, A highly adjustable base isolator utilizing magneotorheological elastomer: experimental testing and modeling, J. Vib. Acoust. 137 (2015) 011009.

DOI: 10.1115/1.4027626

Google Scholar

[7] Y. Yu, Y. Li, J. Li, Parameter identification of a novel strain stiffening model for magnetorheological elastomer base isolator utilizing enhanced particle swarm optimization, J Intel. Mat. Syst. Str. doi: 10. 1177/1045389X14556166.

DOI: 10.1177/1045389x14556166

Google Scholar

[8] Y. Li, J. Li, T. Tian, W. Li, A highly adjustable magnetorheological elastomer base isolator for applications of real-time adaptive control, Struct. 22 (2013) 095020.

DOI: 10.1088/0964-1726/22/9/095020

Google Scholar

[9] J. Yen and R. Langari, Fuzzy Logic: Intelligence Control, and Information. New York, NY: Prentice Hall, (1999).

Google Scholar

[10] Y. Yu, Y. Li, J. Li, Nonparametric modeling of magnetorheological elastomer base isolator based on artificial neural network optimized by ant colony algorithm, J Intel. Mat. Syst. Str. 26 (2015) 1789-1798.

DOI: 10.1177/1045389x15577649

Google Scholar

[11] Y. Yu, Y. Li, J. Li, Forecasting hysteresis behaviours of magnetorheological elastomer base isolator utilizing a hybrid model based on support vector regression and improved particle swarm optimization, Struct. 22 (2013) 035005.

DOI: 10.1088/0964-1726/24/3/035025

Google Scholar

[12] D. Murray_smith, Methods for the external validation of continuous system simulation models: a review, Math. Comput. Model. Dyn. Syst. 4 (1998) 5–31.

Google Scholar