Genetic Algorithm based Optimization approach for MR Dampers Fuzzy Modeling
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.
[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. Bäck, 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-Guervós, J. Bullinaria, J. Rowe, P. Tino, A. Kabán, 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.
[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. Bäck, 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-Guervós, J. Bullinaria, J. Rowe, P. Tino, A. Kabán, 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.
@article{"International Journal of Architectural, Civil and Construction Sciences:60311", author = "Behnam Mehrkian and Arash Bahar and Ali Chaibakhsh", title = "Genetic Algorithm based Optimization approach for MR Dampers Fuzzy Modeling", abstract = "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.", volume = "5", number = "11", pages = "610-7", }
