Dynamic Behavior Prediction of Magnetorheological Fluid Dampers using Neural Networks

Amr Atef Abd Elwahed; Hassan Metered; Hany Monieb

Amr Atef Abd Elwahed
Hassan Metered
Hany Monieb

Keywords: Magnetorheological Dampers, Neural Networks, Nonparametric Identification, Nonlinear Systems

Abstract

Parametric and non-parametric models have been studied to predict the behavior of magnetorheological (MR) fluid dampers by a lot of researchers due to their nonlinear dynamics. In this paper, the direct and inverse dynamic identification for MR fluid dampers using recurrent neural networks are investigated to demonstrate the more accurate and efficient model. The effect of neural networks construction on the prediction quality of dynamic performance of MR damper is introduced in details. The trained direct identification neural network model can be used to predict the damping force of the MR fluid damper on line and the inverse dynamic neural network model can be used to generate the command voltage applied to the damper coil through supervised learning. The architectures and the learning techniques of direct and inverse neural network models for MR fluid dampers are introduced and simulation results are discussed. Finally, the trained neural network models are used to predict the damping force of the MR fluid damper accurately and precisely. Moreover, validation results for the neural network models are proposed and used to evaluate their performance. Validation results with several data sets indicate that the proposed direct and inverse identification models using recurrent neural networks can be used to predict the dynamic performance of MR fluid dampers perfectly.

References

Bani-Hani, K. A. & Sheban, M. A. (2006). Semiactive neuro-control for base-isolation system using magnetorheological (MR) dampers. Earthquake Engineering and Structural Dynamics, 35, 1119–1144.
Bittanti, S., Savaresi, S.M., & Montiglio, M. (2004). Neural-Network Model of a Magneto-Rheological Damper. IFAC Proceedings, 37(16), 55–60.
Boada, M.J.L., Calvo, J.A., Boada, B.L., & Díaz, V. (2011). Modeling of a magnetorheological damper by recursive lazy learning. Int. J. Nonlinear Mech., 46(3), 479–485.
Chang, C.-C. & Roschke, P. (1998). Neural Network Modeling of a Magnetorheological Damper. J. Intell. Mater. Syst. Struct., 9(9), 755–764.
Du, H., Lam, J., & Zhang, N. (2006). Modelling of a magneto-rheological damper by evolving radial basis function networks. Eng. Appl. Artif. Intell., 19(8), 869–881.
Dyke, S.J., Spencer, B.F., Jr, Sain, M.K., & Carlson, J.D. (1998). An experimental study of MR dampers for seismic protection. Smart Mater. Struct., 7(5), 693– 703.
Karkoub, M. A. & Zribi, M. (2006). Active / semiactive suspension control using magnetorheological actuators. International Journal of Systems Science, 37(1), 35–44.
Khalid, M. (2014). Nonlinear identification of a magneto-rheological damper based on dynamic neural networks. Comput.-Aided Civ. Infrastruct. Eng., 29(2), 221–233.
Liao, W. H. & Wang, D. H. (2003). Semiactive vibration control of train suspension systems via magnetorheological dampers. Intelligent Material Systems and Structures, 14, 161–172.
Liao, W. H. & Wang, D. H. (2005). Modeling and control of magnetorheological fluid dampers using neural networks. Journal of Smart Materials and Structures, 14, 111–126.
Luong, Q.V., Jang, D.-S., & Hwang, J.-H. (2020). Robust Adaptive Control for an Aircraft Landing Gear Equipped with a Magnetorheological Damper. Appl. Sci., 10, 1459. https://doi.org/10.3390/app10041459.
Ma, X.Q., Wang, E.R., Rakheja, S., & Su, C.Y. (2002). Modeling hysteretic characteristics of MR-fluid damper and model validation. 41st IEEE Conference on Decision and Control, NV, USA, IEEE, pp. 1675–1680.
Metered, H., Bonello, P., & Oyadiji, S. O. (2010). The experimental identification of magnetorheological dampers and evaluation of their controllers. Mech. Syst. Sig. Process. 24 (4), 976–994.
Sahin, I., Engin, T., & Çesmeci, S. (2010). Comparison of some existing parametric models for magnetorheological fluid dampers. Smart Mater. Struct., 19(3), 035012. https://doi.org/10.1088/0964-1726/19/3/035012.
Shehata Gad, A. & El-Demerdash, S. (2022). Effect of Optimal Fuzzy Models for Pneumatic Magnetorheological Suspension System on Ride Performance under Different Conditions. SAE Int. J. Veh. Dyn., Stab., and NVH, 6(4), 421-440. https://doi.org/10.4271/10-06-04-0028.
Spencer, B.F., Jr., Dyke, S.J., Sain, M.K., & Carlson, J.D. (1997). Phenomenological model for magnetorheological dampers. Journal of Engineering Mechanics, American Society of Civil Engineers, 123(3), 230–238.
Tudón-Martínez, J.C., Lozoya-Santos, J.J., Morales-Menendez, R., & Ramirez-Mendoza, R.A. (2012). An experimental artificial-neural-network-based modeling of magneto-rheological fluid dampers. Smart Mater. Struct., 21(8), 085007.
Turcotte, J., East, W., & Plante, J. (2022). Experimental Assessment of a Controlled Slippage Magnetorheological Automotive Active Suspension for Ride Comfort. SAE Int. J. Veh. Dyn., Stab., and NVH, 6(4), 357-370. https://doi.org/10.4271/10-06-04-0024.
Wang, D.H. & Liao, W.H. (2011). Magnetorheological fluid dampers: a review of parametric modelling. Smart Mater. Struct., 20(2), 023001. https://doi.org/10.1088/0964-1726/20/2/023001.
Wang, E.R., Ma, X.Q., Rakheja, S., & Su, C.Y. (2003). Modeling hysteretic characteristics of an MR-fluid damper. Proc. of the Inst. of Mech, Engrs. Part D: J. of Automobile Engineering, 217, 537–550.
Xiao Qing Ma, Rakheja, S. & Su, C.-Y. (2007). Development and Relative Assessments of Models for Characterizing the Current Dependent Hysteresis Properties of Magnetorheological Fluid Dampers. J. Intell. Mater. Syst. Struct., 18(5), 487–502.
Yang, G. (2001). Large-scale magnetorheological fluid damper for vibration mitigation: modeling, testing and control. Ph.D. dissertation, University of Notre Dame.
Yao, G. Z., Yap, F. F., Chen, G., Li, W., & Yeo, S. H. (2002). MR damper and its application for semi-active control of vehicle suspension system. Mechatronics, 12(7), 963-973.