Characterization and modeling of a self-sensing MR damper under harmonic loading

  • Chen, Z.H. (College of Civil Engineering, Fuzhou University) ;
  • Ni, Y.Q. (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University) ;
  • Or, S.W. (Department of Electrical Engineering, The Hong Kong Polytechnic University)
  • Received : 2014.05.19
  • Accepted : 2014.11.10
  • Published : 2015.04.25


A self-sensing magnetorheological (MR) damper with embedded piezoelectric force sensor has recently been devised to facilitate real-time close-looped control of structural vibration in a simple and reliable manner. The development and characterization of the self-sensing MR damper are presented based on experimental work, which demonstrates its reliable force sensing and controllable damping capabilities. With the use of experimental data acquired under harmonic loading, a nonparametric dynamic model is formulated to portray the nonlinear behaviors of the self-sensing MR damper based on NARX modeling and neural network techniques. The Bayesian regularization is adopted in the network training procedure to eschew overfitting problem and enhance generalization. Verification results indicate that the developed NARX network model accurately describes the forward dynamics of the self-sensing MR damper and has superior prediction performance and generalization capability over a Bouc-Wen parametric model.


  1. Ahn, K.K., Truong, D.Q. and Islam, M.A. (2009), "Modeling of a magneto-rheological (MR) fluid damper using a self tuning fuzzy mechanism", J. Mech. Sci. Technol., 23(5), 1485-1499.
  2. Boada, M.J.L., Calvo, J.A., Boada, B.L. and Diaz, V. (2011), "Modeling of a magnetorheological damper by recursive lazy learning", Int. J. Nonlinear Mech., 46(3), 479-485.
  3. Boston, C., Weber, F. and Guzzella, L. (2010), "Modeling of a disc-type magnetorheological damper", Smart Mater. Struct., 19(4), 045005.
  4. Cao, M., Wang, K.W. and Lee, K.Y. (2008), "Scalable and invertible PMNN model for magneto-rheological fluid dampers", J. Vib. Control, 14(5), 731-751.
  5. Carlson, J.D., Catanzarite, D.M. and Clair, K.A.St. (1996), "Commercial magneto-rheological fluid devices", Int. J. Modern Phys. B, 10(23-24), 2857-2865.
  6. Chang, C.C. and Roschke, P.N. (1998), "Neural network modeling of a magnetorheological damper", J. Intel. Mater. Syst. Struct., 9(9), 755-764.
  7. Chen, S., Billings, S.A., Cowan, C.F. and Grant, P.M. (1990), "Practical identification of NARMAX models using radial basis functions", Int. J. Control, 52(6), 1327-1350.
  8. Chen, Z.Q., Wang, X.Y., Ko, J.M., Ni, Y.Q., Spencer, B.F., Yang, G. and Hu, J.H. (2004), "MR damping system for mitigating wind-rain induced vibration on Dongting Lake Cable-Stayed Bridge", Wind Struct., 7(5), 293-304.
  9. Choi, S.B., Seong, M.S. and Ha, S.H. (2009), "Vibration control of an MR vehicle suspension system considering both hysteretic behavior and parameter variation", Smart Mater. Struct., 18(12), 125010.
  10. Duan, Y.F., Ni, Y.Q. and Ko, J.M. (2005), "State-derivative feedback control of cable vibration using semiactive magnetorheological dampers", Comput. - Aided Civil Infrastruct. Eng., 20(6), 431-449.
  11. Dyke, S.J., Spencer, B.F. Jr., Sain, M.K. and Carlson, J.D. (1996), "Modeling and control of magnetorheological dampers for seismic response reduction", Smart Mater. Struct., 5(5), 565-575.
  12. Foresee, F.D. and Hagan, M.T. (1997), "Gauss-Newton approximation to Bayesian learning", Proceedings of the 1997 IEEE International Joint Conference on Neural Networks, Houston, USA, June.
  13. Fujitani, H., Sodeyama, H., Tomura, T., Hiwatashi, T., Shiozaki, Y., Hata, K., Sunakoda, K., Morishita, S. and Soda, S. (2003), "Development of 400kN magnetorheological damper for a real base-isolated building", Proceedings of the SPIE, Smart Structures and Materials 2003: Damping and Isolation, (Eds., G.S. Agnes and K.W. Wang), San Diego, CA, USA, March.
  14. Gandhi, F., Wang, K.W. and Xia, L. (2001), "Magnetorheological fluid damper feedback linearization control for helicopter rotor application", Smart Mater. Struct., 10(1), 96-103.
  15. Gordaninejad, F., Saiidi, M., Hansen, B.C., Ericksen, E.O. and Chang, F.-K. (2002), "Magneto-rheological fluid dampers for control of bridges", J. Intel. Mat. Syst. Str., 13(2-3), 167-180.
  16. Hagan, M.T. and Menhaj, M.B. (1994), "Training feedforward networks with the Marquardt algorithm", IEEE T. Neural Networ., 5(6), 989-993.
  17. Hornik, K., Stinchcombe, M. and White, H. (1989), "Multilayer feedforward networks are universal approximators", Neural Networks, 2(5), 359-366.
  18. Hu, W. and Wereley, N.M. (2008), "Hybrid magnetorheological fluid-elastomeric lag dampers for helicopter stability augmentation", Smart Mater. Struct., 17(4), 045021.
  19. Ikhouane, F. and Dyke, S.J. (2007), "Modeling and identification of a shear mode magnetorheological damper", Smart Mater. Struct., 16(3), 605-616.
  20. Jimenez, R. and Alvarez-Icaza, L. (2005), "LuGre friction model for a magnetorheological damper", Struct. Control Health Monit., 12(1), 91-116.
  21. Jin, G., Sain, M.K. and Spencer, B.F. Jr. (2005), "Nonlinear blackbox modeling of MR-dampers for civil structural control", IEEE T. Contr. Sys. T., 13(3), 345-355.
  22. Johnson, E.A., Baker, G.A., Spencer, B.F. Jr. and Fujino, Y. (2000), "Mitigating stay cable oscillation using semiactive damping", Proceedings of the SPIE, Smart Structures and Materials 2000: Smart Systems for Bridges, Structures, and Highways, S.-C. Liu (ed.), Newport Beach, USA, March.
  23. Jung, H.J., Spencer, B.F. Jr. and Lee, I.W. (2003), "Control of seismically excited cable-stayed bridge employing magnetorheological fluid dampers", J. Struct. Eng. - ASCE, 129(7), 873-883.
  24. Jung, H.J., Spencer, B.F. Jr., Ni, Y.Q. and Lee, I.W. (2004), "State-of-the-art of semiactive control systems using MR fluid dampers in civil engineering applications", Struct. Eng. Mech., 17(3), 493-526.
  25. Karimi, H.R., Zapateiro, M. and Luo, N. (2009), "Wavelet-based parameter identification of a nonlinear magnetorheological damper", Int. J. Wavelets Multi., 7(2), 183-198.
  26. Ko, J.M., Ni, Y.Q., Chen, Z.Q. and Spencer, B.F. Jr. (2002), "Implementation of magneto-rheological dampers to Dongting Lake Bridge for cable vibration mitigation", Proceedings of the 3rd World Conference on Structural Control, F. Casciati (ed.), Como, Italy, April.
  27. Leva, A. and Piroddi, L. (2002), "NARX-based technique for the modelling of magneto-rheological damping devices", Smart Mater. Struct., 11(1), 79-88.
  28. Li, H., Liu, M., Li, J., Guan, X. and Ou, J. (2007), "Vibration control of stay cables of the Shandong Binzhou Yellow River Highway Bridge using magnetorheological fluid dampers", J. Bridge Eng., 12(4), 401-409.
  29. Liao, W.H. and Wang, D.H. (2003), "Semiactive vibration control of train suspension systems via magnetorheological dampers", J. Intel. Mat. Syst. Str., 14(3), 161-172.
  30. Loh, C.H., Lynch, J.P., Lu, K.C. and Wang, Y. (2007), "Experimental verification of a wireless sensing and control system for structural control using MR dampers", Earthq. Eng. Struct. D., 36(10), 1303-1328.
  31. MacKay, D.J.C. (1992), "A practical Bayesian framework for backprop networks", Neural Comput., 4(3), 448-472.
  32. Ni, Y.Q., Ying, Z.G., Wang, J.Y., Ko, J.M. and Spencer, B.F. Jr. (2004), "Stochastic optimal control of wind-excited tall buildings using semi-active MR-TLCDs", Probabilist. Eng. Mech., 19(3), 269-277.
  33. Or, S.W., Duan, Y.F., Ni, Y.Q., Chen, Z.H. and Lam, K.H. (2008), "Development of magnetorheological dampers with embedded piezoelectric sensors for structural vibration control", J. Intel. Mat. Syst. Str., 19(11), 1327-1338.
  34. Pang, L., Kamath, G.M. and Wereley, N.M. (1998), "Analysis and testing of a linear stroke magnetorheological damper", Proceedings of the AIAA/ASME/AHS Adaptive Structures Forum, Long Beach, CA, April.
  35. Schurter, K.C. and Roschke, P.N. (2000), "Fuzzy modeling of a magnetorheological damper using ANFIS", Proceedings of the 9th IEEE International Conference on Fuzzy Systems, San Antonio, USA, May.
  36. Sjoberg, J. and Ljung, L. (1995), "Overtraining, regularization and searching for minimum, with application to neural networks", Int. J. Control, 62(6), 1391-1407.
  37. Song, X., Ahmadian, M., Southward, S. and Miller, L.R. (2005), "An adaptive semiactive control algorithm for magnetorheological suspension systems", J. Vib. Acoust., 127(5), 493-502.
  38. Spencer, B.F. Jr., Dyke, S.J., Sain, M.K. and Carlson, J.D. (1997), "Phenomenological model for magnetorheological dampers", J. Eng. Mech.- ASCE, 123(3), 230-238.
  39. Suykens, J.A.K., Vandewalle, J.P.L. and De Moor, B.L.R. (1996), Artificial Neural Networks for Modeling and Control of Non-linear Systems, Kluwer Academic Publishers, Boston, USA.
  40. Wang, D.H. and Liao, W.H. (2005), "Modeling and control of magnetorheological fluid dampers using neural networks", Smart Mater. Struct., 14(1), 111-126.
  41. Weber, F., Distl, H., Feltrin, G. and Motavalli, M. (2005a), "Evaluation procedure of decay measurements of a cable with passive-on operating MR damper", Proceedings of the 6th International Symposium on Cable Dynamics, Charleston, USA, September.
  42. Weber, F., Distl, H., Feltrin, G. and Motavalli, M. (2005b), "Simplified approach of velocity feedback for MR dampers on real cable-stayed bridges", Proceedings of the 6th International Symposium on Cable Dynamics, Charleston, USA, September.
  43. Ying, Z.G., Ni, Y.Q. and Ko, J.M. (2005), "Semi-active optimal control of linearized systems with multi-degree of freedom and application", J. Sound Vib., 279(1-2), 373-388.

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