DOI QR코드

DOI QR Code

Real-time model updating for magnetorheological damper identification: an experimental study

  • Song, Wei (Department of Civil, Construction and Environmental Engineering, The University of Alabama) ;
  • Hayati, Saeid (Department of Civil, Construction and Environmental Engineering, The University of Alabama) ;
  • Zhou, Shanglian (Department of Civil, Construction and Environmental Engineering, The University of Alabama)
  • Received : 2017.07.04
  • Accepted : 2017.09.20
  • Published : 2017.11.25

Abstract

Magnetorheological (MR) damper is a type of controllable device widely used in vibration mitigation. This device is highly nonlinear, and exhibits strongly hysteretic behavior that is dependent on both the motion imposed on the device and the strength of the surrounding electromagnetic field. An accurate model for understanding and predicting the nonlinear damping force of the MR damper is crucial for its control applications. The MR damper models are often identified off-line by conducting regression analysis using data collected under constant voltage. In this study, a MR damper model is integrated with a model for the power supply unit (PSU) to consider the dynamic behavior of the PSU, and then a real-time nonlinear model updating technique is proposed to accurately identify this integrated MR damper model with the efficiency that cannot be offered by off-line methods. The unscented Kalman filter is implemented as the updating algorithm on a cyber-physical model updating platform. Using this platform, the experimental study is conducted to identify MR damper models in real-time, under in-service conditions with time-varying current levels. For comparison purposes, both off-line and real-time updating methods are applied in the experimental study. The results demonstrate that all the updated models can provide good identification accuracy, but the error comparison shows the real-time updated models yield smaller relative errors than the off-line updated model. In addition, the real-time state estimates obtained during the model updating can be used as feedback for potential nonlinear control design for MR dampers.

Keywords

References

  1. Bass, B.J. and Christenson, R.E. (2007), "System ientification of a 200 kN mgneto-reological fuid dmper for sructural cntrol in lrge-sale sart sructures", Proceedings of the 2007 American Control Conference, New York City, USA.
  2. Chatzi, E.N. and Smyth, A.W. (2008), "The unscented kalman filter and particle filter methods for nonlinear structural system identification with non-collocated heterogeneous sensing", Struct. Control Health., 16(1), 99-123.
  3. Chatzi, E.N., Smyth, A.W. and Masri, S.F. (2010), "Experimental application of on-line parametric identification for nonlinear Hysteretic Systems with Model Uncertainty", Struct. Saf., 32(5), 326-337. https://doi.org/10.1016/j.strusafe.2010.03.008
  4. Choi, S., Lee, S. and Park, Y. (2001), "A hysteresis model for the field-dependent damping force of a magnetorheological damper", J. Sound Vib., 245(2), 375-383. https://doi.org/10.1006/jsvi.2000.3539
  5. Dyke, S.J. (1996), "Acceleration feedback control strategies for active and semi-active control systems: modeling, algorithm development, and experimental verification", Ph.D. Dissertation, Notre Dame University, Indiana.
  6. Dyke, S.J., Spencer Jr., B.F., 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. https://doi.org/10.1088/0964-1726/5/5/006
  7. Dyke, S.J., Spencer Jr., B.F., Sain, M.K. and Carlson, J.D. (1998), "An experimental study of MR dampers for seismic protection", Smart Mater. Struct., 7(5), 693-703. https://doi.org/10.1088/0964-1726/7/5/012
  8. Gavin, H.P. (2001), "Multi-duct ER Dampers", J. Intel. Mat. Syst. Struct., 12(5), 353-366. https://doi.org/10.1106/8398-U3X9-DHK9-K304
  9. Ghanem, R. and Shinozuka, M. (1995a), "Structural System Identification. I: Theory", J. Eng. Mech., 121(2), 255-264. https://doi.org/10.1061/(ASCE)0733-9399(1995)121:2(255)
  10. Ghanem, R. and Shinozuka, M. (1995b), "Structural System Identification. II: Experimental Verification", J. Eng. Mech., 121(2), 265-273. https://doi.org/10.1061/(ASCE)0733-9399(1995)121:2(265)
  11. Hashemi, M.J., Masroor, A. and Mosqueda, G. (2014), "Implementation of online model updating in hybrid simulation", Earthq. Eng. Struct. D., 43(3), 395-412. https://doi.org/10.1002/eqe.2350
  12. Hoshiya, M. and Saito, E. (1984), "Structural identification by extended kalman filter", J. Eng. Mech., 110(12), 1757-1772. https://doi.org/10.1061/(ASCE)0733-9399(1984)110:12(1757)
  13. Ikhouane, F. and Rodellar, J. (2007), Systems with Hysteresis: Analysis, Identification and Control Using the Bouc-Wen Model, Wiley-Interscience.
  14. Jiang, Z. and Christenson, R. (2011), "A comparison of 200 kN magneto-rheological damper models for use in real-time hybrid simulation pretesting", Smart Mater. Struct., 20(6), 1-11.
  15. Jiang, Z. and Christenson, R.E. (2012), "A fully dynamic magneto-rheological fluid damper model", Smart Mater. Struct., 21(6), 065002 (065012pp). https://doi.org/10.1088/0964-1726/21/6/065002
  16. Julier, S.J., Uhlmann, J.K. and Durrant-Whyte, H.F. (1995), "A new approach for filtering nonlinear systems", Proceedings of the 1995 American Control Conference, Seattle, Washington.
  17. Julier, S.J. (2002), "The scaled unscented transformation", Proceedings of the 2002 American Control Conference, Anchorage, AK.
  18. Lin, J.W., Betti, R., Smyth, A.W. and Longman, R.W. (2001), "On-line identification of non-linear hysteretic structural systems using a variable trace approach", Earthq. Eng. Struct. D., 30(9), 1279-1303. https://doi.org/10.1002/eqe.63
  19. Lin, P.Y., Roschke, P. and Loh, C.H. (2005), "System identification and real application of a smart magnetorheological damper", Proceedings of the 2005 IEEE International Symposium on Intelligent Control, Limassol, Cyprus.
  20. MATHWORKS (2016), Manual of MATLAB, MATHWORKS.
  21. Ou, G., Dyke, S.J. and Prakash, A. (2017), "Real time hybrid simulation with online model updating: An analysis of accuracy", Mech. Syst. Signal. Pr., 84B, 223-240.
  22. Rodriguez, A., Iwata, N., Ikhouane, F. and Rodellar, J. (2009), "Model identification of a large-scale magnetorheological fluid damper", Smart Mater. Struct., 18(1), 1-12.
  23. Ruangrassamee, A., Srisamai, W. and Lukkunaprasit, P. (2006), "Response mitigation of the base Isolated benchmark building by semi-active control with the viscous-plus-variable-friction damping force algorithm", Struct. Control Health., 13(2-3), 809-822. https://doi.org/10.1002/stc.113
  24. Shao, X., Mueller, A. and Mohammed, B.A. (2016), "Real-time hybrid simulation with online model updating: methodology and implementation", J. Eng. Mech., 142(2), 04015074-04015071-04015019.
  25. Smyth, A.W., Masri, S.F., Chassiakos, A.G. and Caughey, T.K. (1999), "On-line parameteric identification of MDOF nonlinear hysteretic systems", J. Eng. Mech., 125(2), 133-142. https://doi.org/10.1061/(ASCE)0733-9399(1999)125:2(133)
  26. Song, W., Dyke, S., Yun, G. and Harmon, T. (2009), "Improved damage localization and quantification using subset selection", J. Eng. Mech., 135(6), 548-560. https://doi.org/10.1061/(ASCE)EM.1943-7889.0000005
  27. Song, W. and Dyke, S.J. (2010), "Application of nonlinear observers in hysteretic model updating", Proceeding of SPIE, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2010, San Diego, CA, USA
  28. Song, W. (2011), "Dynamic model updating with applications in structural and damping systems: from linear to nonlinear, from off-line to real-time", Ph.D. Dissertation, Purdue University, Indiana.
  29. Song, W., Dyke, S. and Harmon, T. (2013), "Application of nonlinear model updating for a reinforced concrete shear wall", J. Eng. Mech., 139(5), 635-649. https://doi.org/10.1061/(ASCE)EM.1943-7889.0000519
  30. Song, W. and Dyke, S. (2014), "Real-time dynamic model updating of a hysteretic structural system", J. Struct. Eng., 140(3), 04013082-04013081-04013014.
  31. Song, X., Ahmadian, M. and Southward, S.C. (2005), "Modeling magnetorheological dampers with application of nonparametric approach", J. Intel. Mat. Syst. Str., 16(5), 421-432. https://doi.org/10.1177/1045389X05051071
  32. Spencer Jr., B.F., Dyke, S.J., Sain, M.K. and Carlson, J.D. (1997), "Phenomenological model for magnetorheological dampers", J. Eng. Mech., 123(3), 230-238. https://doi.org/10.1061/(ASCE)0733-9399(1997)123:3(230)
  33. Spencer Jr., B.F. and Nagarajaiah, S. (2003), "State of the art of structural control", J. Struct. Eng., 129(7), 845-856. https://doi.org/10.1061/(ASCE)0733-9445(2003)129:7(845)
  34. Stanway, R., Sproston, J. and Stevens, N. (1987), "Non-linear modeling of an electro-rheological vibration damper", J. Electrostat., 20(2), 167-184. https://doi.org/10.1016/0304-3886(87)90056-8
  35. Van der Merwe, R. and Wan, E. (2003), "Gaussian mixture sigma-point particle filters for sequential pProbabilistic inference in dynamic state-space models", Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Hong Kong.
  36. Wereley, N. and Pang, L. (1998), "Nondimensional analysis of semi-active electrorheological and magnetorheological dampers using approximate parallel plate models", Smart Mater. Struct., 7, 732-743. https://doi.org/10.1088/0964-1726/7/5/015
  37. Wu, B. and Wang, T. (2014), "Model updating with constrained unscented Kalman filter for hybrid testing", Smart Struct. Syst., 14(6), 1105-1129. https://doi.org/10.12989/sss.2014.14.6.1105
  38. Wu, M. and Smyth, A.W. (2007), "Application of the unscented kalman filter for real-time nonlinear structural system identification", Struct. Control Health., 14, 971-990. https://doi.org/10.1002/stc.186
  39. Wu, M. and Smyth, A. (2008), "Real-time parameter estimation for degrading and pinching hysteretic models", Int. J. Nonlinear Mech., 43(9), 822-833. https://doi.org/10.1016/j.ijnonlinmec.2008.05.010
  40. Yang, G. (2001), "Large-scale magnetorheological fluid damper for vibration mitigation: modeling, testing and control", Ph.D. Dissertation, Notre Dame University, Indiana.
  41. Yang, J.N., Lin, S., Huang, H. and Zhou, L. (2006), "An adaptive extended kalman filter for structural damage identification", Struct. Control Health., 13(4), 849-867. https://doi.org/10.1002/stc.84
  42. Yao, J.T.P. (1972), "Concept of structural control", J. Struct. Div., 98(7), 1567-1574.
  43. Yun, C.B. and Shinozuka, M. (1980), "Identification of nonlinear structural dynamic systems", J. Struct. Mech., 8(2), 187-203. https://doi.org/10.1080/03601218008907359