Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32716
Genetic Algorithm based Optimization approach for MR Dampers Fuzzy Modeling

Authors: Behnam Mehrkian, Arash Bahar, Ali Chaibakhsh


Magneto-rheological (MR) fluid damper is a semiactive control device that has recently received more attention by the vibration control community. But inherent hysteretic and highly nonlinear dynamics of MR fluid damper is one of the challenging aspects to employ its unique characteristics. The combination of artificial neural network (ANN) and fuzzy logic system (FLS) have been used to imitate more precisely the behavior of this device. However, the derivative-based nature of adaptive networks causes some deficiencies. Therefore, in this paper, a novel approach that employ genetic algorithm, as a free-derivative algorithm, to enhance the capability of fuzzy systems, is proposed. The proposed method used to model MR damper. The results will be compared with adaptive neuro-fuzzy inference system (ANFIS) model, which is one of the well-known approaches in soft computing framework, and two best parametric models of MR damper. Data are generated based on benchmark program by applying a number of famous earthquake records.

Keywords: Benchmark program, earthquake record filtering, fuzzy logic, genetic algorithm, MR damper.

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2039


[1] Soong T T and Dargush G F 1997 Passive Energy Dissipation Systems in Structural Engineering (UK: Wiley).
[2] Soong T T and Spencer B F Jr 2002 Supplemental energy dissipation: state-of-the-art and state-of-the-practice Eng.Struct. 24 243259.
[3] Choi S B, Lee S K and Park Y P 2001 A hysteresis model for fielddependent damping force of a magnetorheological damper J. Sound Vib. 245 375-83.
[4] Jolly M R, Bender J W and Carlson J D 1999 Properties and applications of commercial magnetorheological fluids J. Int. Mater. Syst. Struct. 101 5-13.
[5] Spencer B F Jr, Dyke S J, Sain M K and Carlson J D 1997 Phenomenological model for a magnetorheological damper ASCE J.Eng. Mech. 123 230-52.
[6] Stanway R, Sproston J L and Stevens N G 1987 Non-linear modelling of an electro-rheological vibration damper J. Electrostat. 20 167-84.
[7] Gamota D R and Filisko F E 1991 Dynamic mechanical studies of electrorheological materials: moderate frequencies J. Rheol. 35 399- 425.
[8] G. Yang, Large-scale magnetorheological fluid damper for vibration mitigation: Modeling, testing and control, Ph.D dissertation,University of Notre Dame (2001).
[9] M. Kciuk and R. Turczyn, Properties and application of magnetorheological fluids, Journal of Achievement in Material and Manufacturing Engineering 18 (2006), no. 1-2, 127-130.
[10] Wen Y K 1976 Method of random vibration of hysteretic systems ASCE J. Eng. Mech. 102 249-63.
[11] Dahl P R 1968 A solid friction model Technical Report TOR-158(3107- 18) (El-Segundo, CA: The Aerospace Corporation)
[12] Ikhouane F and Dyke S J 2007 Modeling and identification of a shear mode magnetorheological damper Smart Mater. Struct.16 605-16.
[13] Jimenez R and Alvarez-Icaza L 2004 LuGre friction model for a magnetorheological damper Struct. Cont. Health Monit.20 91-116.
[14] Guo S, Yang S and Pan C 2006 Dynamic modeling of magnetorheological damper behaviors J. Int. Mater. Syst.Struct. 17 3- 14.
[15] Ikhouane F, Rodellar J. Systems with hysteresis: analysis, identification and control using the Bouc-Wen model. John Wiley and Sons Inc.; 2007.
[16] Rodriguez A, Ikhouane F, Rodellar J, Luo N. Modeling and identification of a small-scale magnetorheological damper. J Intell Mater Syst Struct 2009;20(7):825-35.
[17] Bahar A, Pozo F, Acho L, Rodellar J, Barbat A. Parameter identification of large scale magnetorheological dampers in a benchmark building. Computers and Structures 2010;88:198-206.
[18] Schurter KC, Roschke PN. Fuzzy modelling of a magnetorheological damper using ANFIS. In: Proc. 9th IEEE intl conf on fuzzy systems. 2000.p. 122-7.
[19] Wang DH, Liao WH. Modeling and control of magnetorheological fluid dampers using neural networks. Smart Materials and Structures 2005;14(1):111-26.
[20] Jin G, Sian MK, Spencer Jr BF. Nonlinear blackbox modeling of MRdampers for civil structural control. IEEE Transactions on Control Systems Technology 005;13(3):345-55.
[21] Choi SB, Lee SK. A hysteresis model for the field-dependent damping force of a magnetorheological damper. Journal of Sound and Vibration 2001;245(2):375-83.
[22] Ma XQ,Wang ER, Rakheja S, Su CY. Modeling hysteretic characteristics of MR-fluid damper and model validation. In: Proc. 41st IEEE conf. On decision and control. 2002. p. 1675-80.
[23] Giuclea M, Sireteanu T, Stanciou D, Stammers CW. Model parameter identification of vehicle vibration control with magnetorheologicaldampers using computational intelligent methods. Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering 2004;218:569-81.
[24] N.M. Kwok_, Q.P. Ha, M.T. Nguyen, J. Li, B. Samali Bouc-Wen model parameter identification for a MR fluid damper using computationally efficient GA. 0019-0578/$ - see front matterc 2007, ISA. Published by Elsevier Ltd. p.167-179.
[25] Narasimhan S, Nagarajaiah S, Johnson EA, Gavin HP. Smart baseisolated benchmark building. Part I: problem definition. Struct Control Health Monitor 2006;13(2-3):573-88.
[26] J.H. Holland. Adaptation in natural and artificial systems. University of Michigan Press,Michigan ,1975.
[27] Goldberg DE. Genetic algorithms in search, optimization and machine learning. Reading (MA): Addison-Wesley; 1989.
[28] Jang JR, Sun CT, Mizutani E. Neuro-fuzzy and soft computing. Prentice-Hall International Inc. 1997.
[29] Eiben AE, Hinterding R, Michalewicz Z. Parameter control in evolutionary. algorithms. IEEE Transactions on Evolutionary Computation 1999;3(2):124-41.
[30] Matthew S. Gibbs,1, Graeme C. Dandy, Holger R. Maier. A genetic algorithm calibration method based on convergence due to genetic drift.0020-0255/$ - see front matter _ 2008 Elsevier Inc. p.2857-2869.
[31] J. Arabas, Z. Michalewicz, J.J. Mulawka, GAVaPS - a genetic algorithm with varying population size., in: International Conference on Evolutionary Computation, 1994, pp. 73-78.
[32] T. B├ñck, A.E. Eiben, N. van der Vaart, An empirical study on GAs ÔÇÿÔÇÿwithout parameters", in: M. Schoenauer, K. Deb, G. Rudolph, X. Yao, E. Lutton,J.J. Merelo, H.-P. Schwefel (Eds.), Parallel Problem Solving from NatureÔÇöPPSN VI of Lecture Notes in Computer Science, vol. 1917, Springer-Verlag,Paris, France, 2000, pp. 315-324.
[33] A.E. Eiben, E. Marchiori, V.A. Valko, Evolutionary algorithms with onthe- fly population size adjustment, in: X. Yao, E. Burke, J. Lozano,J. Smith, J. Merelo-Guerv├│s, J. Bullinaria, J. Rowe, P. Tino, A. Kab├ín, H.- P. Schwefel (Eds.), Parallel Problem Solving from NatureÔÇöPPSN VIII of Lecture Notes in Computer Science, vol. 3242, Springer, 2004, pp. 41-50.
[34] K. Srinivasa, K. Venugopal, L. Patnaik, A self-adaptive migration model genetic algorithm for data mining applications, Information Sciences 177 (20) (2007) 4295-4313..
[35] T. Takagi and M. Sugeno, "Fuzzy identification of systems and its applications to modeling and control," IEEE Trans. Syst., Man, Cybern., vol. SMC-15, no. 1, pp. 116-132, Feb. 1985.
[36] M. Sugeno and G. T. Kang. Structure identification of fuzzy model. Fuzzy Sets and Systems, 28:15-33, 1988.