DOI QR코드

DOI QR Code

A new hybrid model for MR elastomer device and parameter identification based on improved FOA

  • Yu, Yang (School of Civil and Environmental Engineering, University of Technology Sydney) ;
  • Yousefi, Amir M. (Centre for Infrastructure Engineering, Western Sydney University) ;
  • Yi, Kefu (School of Automotive and Mechanical Engineering, Changsha University of Science and Technology) ;
  • Li, Jianchun (School of Civil and Environmental Engineering, University of Technology Sydney) ;
  • Wang, Weiqiang (College of Water Conservancy and Hydropower Engineering, Hohai University) ;
  • Zhou, Xinxiu (Research Institute for Frontier Science, Beihang University)
  • 투고 : 2021.02.19
  • 심사 : 2021.07.15
  • 발행 : 2021.11.25

초록

A new hysteresis model based on curve fitting method is presented in this work to portray the greatly nonlinear and hysteretic relationships between shear force and displacement responses of the magnetorheological (MR) elastomer base isolator. Compared with classical hysteresis models such as Bouc-Wen or LuGre friction model, the proposed model combines the hyperbolic sine function and Gaussian function to model the hysteretic loops of the device responses, contributing to a great decline of model parameters. Then, an improved fruit fly optimization algorithm (FOA) is proposed to optimize the model parameters, in which a self-adaptive step is employed rather than the fixed step to balance the global and local optimum search abilities of algorithm. Finally, the experimental results of the device under both harmonic and random excitations are used to verify the performance of the proposed hybrid model and parameter identification algorithm with the satisfactory results.

키워드

과제정보

The research described in this paper was funded by the Australian Research Council (Grant No. DP150102636) and the Natural Science Foundation of China (Grant No. 52002036). Besides, Dr. Yancheng Li of University of Technology Sydney is appreciated for the help in device design and test.

참고문헌

  1. Aguirre, N., Ikhouane, F., Rodellar, J. and Christenson, R. (2012), "Parametric identification of the Dahl model for large scale MR dampers", Struct. Control Hlth., 19(3), 332-347. https://doi.org/10.1002/stc.434
  2. Aziz, S.A.A., Mazlan, S.A., Ismail, N.I.N. and Choi, S.-B. (2018), "Implementation of functionalized multiwall carbon nanotubes on magnetorheological elastomer", J. Mater. Sci., 53(14), 10122-10134. https://doi.org/10.1007/s10853-018-2315-3
  3. Bastola, A.K. and Li, L. (2018), "A new type of vibration isolator based on magnetorheological elastomer", Mater. Des., 157, 431-436. https://doi.org/10.1016/j.matdes.2018.08.009
  4. Behrooz, M., Wang, X. and Gordaninejad, F. (2014), "Modeling of a new semi-active/passive magnetorheological elastomer isolator", Smart Mater. Struct., 23(4), 045013. https://doi.org/10.1088/0964-1726/23/4/045013
  5. Besdo, D. and Ihlemann, J. (2003), "Properties of rubberlike materials under large deformations explained by self-organizing linkage patterns", Int. J. Plast., 19(7), 1001-1018. https://doi.org/10.1016/S0749-6419(02)00090-6
  6. Charalampakis, A.E. and Koumousis, V.K. (2008), "Identification of Bouc-Wen hysteretic systems by a hybrid evolutionary algorithm", J. Sound Vib., 314(3-5), 571-585. https://doi.org/10.1016/j.jsv.2008.01.018
  7. Fu, J., Lai, J., Yang, Z., Bai, J. and Yu, M. (2020), "Fuzzy-neural network control for a magnetorheological elastomer vibration isolation system", Smart Mater. Struct., 29(7), 074001. https://doi.org/10.1088/1361-665X/ab874d
  8. Hwang, Y., Lee, C.W., Lee, J. and Jung, H.J. (2020), "Feasibility of a new hybrid base isolation system consisting of MR elastomer and roller bearing", Smart Struct. Syst., Int. J., 25(3), 323-335. https://doi.org/10.12989/sss.2020.25.3.323
  9. Jimenez, R. and Alvarez-Icaza, L. (2005), "LuGre friction model for a magnetorheological damper", Struct. Control Hlth., 12(1), 91-116. https://doi.org/10.1002/stc.58
  10. Kwok, N., Ha, Q., Nguyen, T., Li, J. and Samali, B. (2006), "A novel hysteretic model for magnetorheological fluid dampers and parameter identification using particle swarm optimization", Sensor Actuat. A-Phys., 132(2), 441-451. https://doi.org/10.1016/j.sna.2006.03.015
  11. Leng, D., Sun, S., Xu, K. and Liu, G. (2020), "A physical model of magnetorheological elastomer isolator and its dynamic analysis", J. Intell. Mater. Syst. Struct., 31(9), 1141-1156. https://doi.org/10.1177/1045389x20910272
  12. Li, Y. and Li, J. (2019), "Overview of the development of smart base isolation system featuring magnetorheological elastomer", Smart Struct. Syst., Int. J., 24(1), 37-52. https://doi.org/10.12989/sss.2019.24.1.037
  13. Li, M.W., Geng, J., Hong, W.C. and Zhang, Y. (2018), "Hybridizing chaotic and quantum mechanisms and fruit fly optimization algorithm with least squares support vector regression model in electric load forecasting", Energies, 11(9), 2226. https://doi.org/10.3390/en11092226
  14. Lin, S.M. (2013), "Analysis of service satisfaction in web auction logistics service using a combination of Fruit fly optimization algorithm and general regression neural network", Neural Comput. Appl., 22(3-4), 783-791. https://doi.org/10.1007/s00521-011-0769-1
  15. Mullins, L. and Tobin, N. (1957), "Theoretical model for the elastic behavior of filler-reinforced vulcanized rubbers", Rubber Chem. Technol., 30(2), 555-571. https://doi.org/10.5254/1.3542705
  16. Neshat, M., Sepidnam, G., Sargolzaei, M. and Toosi, A.N. (2014), "Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications", Artif. Intell. Rev., 42(4), 965-997. https://doi.org/10.1007/s10462-012-9342-2
  17. Nguyen, X.B., Komatsuzaki, T., Iwata, Y. and Asanuma, H. (2018), "Robust adaptive controller for semi-active control of uncertain structures using a magnetorheological elastomer-based isolator", J. Sound Vib., 434, 192-212. https://doi.org/10.1016/j.jsv.2018.07.047
  18. Niu, J., Zhong, W., Liang, Y., Luo, N. and Qian, F. (2015), "Fruit fly optimization algorithm based on differential evolution and its application on gasification process operation optimization", Knowl.-Based Syst., 88, 253-263. https://doi.org/10.1016/j.knosys.2015.07.027
  19. Pan, W.T. (2013), "Using modified fruit fly optimisation algorithm to perform the function test and case studies", Connect. Sci., 25(2-3), 151-160. https://doi.org/10.1080/09540091.2013.854735
  20. Piatkowski, T. (2014), "Dahl and LuGre dynamic friction models - The analysis of selected properties", Mech. Mach. Theory, 73, 91-100. https://doi.org/10.1016/j.mechmachtheory.2013.10.009
  21. Sun, S.S., Yang, J., Li, W.H., Du, H., Alici, G., Yan, T.H. and Nakano, M. (2017), "Development of an isolator working with magnetorheological elastomers and fluids", Mech. Syst. Signal Pr., 83, 371-384. https://doi.org/10.1016/j.ymssp.2016.06.020
  22. Tairidis, G., Foutsitzi, G., Koutsianitis, P. and Stavroulakis, G.E. (2016), "Fine tuning of a fuzzy controller for vibration suppression of smart plates using genetic algorithms", Adv. Eng. Softw., 101, 123-135. https://doi.org/10.1016/j.advengsoft.2016.01.019
  23. Trivedi, R.R., Pawaskar, D.N. and Shimpi, R.P. (2016), "Optimization of static and dynamic travel range of electrostatically driven microbeams using particle swarm optimization", Adv. Eng. Softw., 97, 1-16. https://doi.org/10.1016/j.advengsoft.2016.01.005
  24. Wen, Q., Wang, Y. and Gong, X. (2017), "The magnetic field dependent dynamic properties of magnetorheological elastomers based on hard magnetic particles", Smart Mater. Struct., 26(7), 075012. https://doi.org/10.1088/1361-665X/aa7396
  25. Xin, F.-L., Bai, X.-X. and Qian, L.-J. (2017), "Principle, modeling, and control of a magnetorheological elastomer dynamic vibration absorber for powertrain mount systems of automobiles", J. Intell. Mater. Syst. Struct., 28(16), 2239-2254. https://doi.org/10.1177/1045389X16672731
  26. Yang, F., Sedaghati, R. and Esmailzadeh, E. (2009), "Development of LuGre friction model for large-scale magneto-rheological fluid dampers", J. Intell. Mater. Syst. Struct., 20(8), 923-937. https://doi.org/10.1177/1045389X08099660
  27. Yang, J., Du, H., Li, W., Li, Y., Li, J., Sun, S. and Deng, H.X. (2013), "Experimental study and modeling of a novel magnetorheological elastomer isolator", Smart Mater. Struct., 22(11), 117001. https://doi.org/10.1088/0964-1726/22/11/117001
  28. Ying, Z.G., Ni, Y.Q. and Duan, Y.F. (2017), "Stochastic vibration suppression analysis of an optimal bounded controlled sandwich beam with MR visco-elastomer core", Smart Struct. Syst., Int. J., 19(1), 21-31. https://doi.org/10.12989/sss.2017.19.1.021
  29. Yousefi, A.M., Samali, B. and Hajirasouliha, I. (2020), "Experimental and numerical investigations of cold-formed austenitic stainless steel unlipped channels under bearing loads", Thin Wall. Struct., 152, 106768. https://doi.org/10.1016/j.tws.2020.106768
  30. Zhang, X. and Li, W. (2009), "Adaptive tuned dynamic vibration absorbers working with MR elastomers", Smart Struct. Syst., Int. J., 5(5), 517-529. https://doi.org/10.12989/sss.2009.5.5.517
  31. Zhang, X., Lu, X., Jia, S. and Li, X. (2018), "A novel phase angle-encoded fruit fly optimization algorithm with mutation adaptation mechanism applied to UAV path planning", Appl. Soft. Comput., 70, 371-388. https://doi.org/10.1016/j.asoc.2018.05.030
  32. Zhou, X., Sun, J., Li, H., Lu, M. and Zeng, F. (2020), "PMSM open-phase fault-tolerant control strategy based on four-leg inverter", IEEE T. Power Electr., 35(3), 2799-2808. https://doi.org/10.1109/TPEL.2019.2925823
  33. Zhu, W. and Rui, X.T. (2014), "Semiactive vibration control using a magnetorheological damper and a magnetorheological elastomer based on the bouc-wen model", Shock Vib., 2014, 405412. https://doi.org/10.1155/2014/405421