Because of the nonlinear hysteresis characteristics of the magneto-rheological damper, the damper’s inverse model has disadvantages of low fitting accuracy and poor practicality. Therefore, in this study, an optimized genetic algorithm has been proposed to optimize the back propagation neural network’s initial weights and threshold. Compared with other damper controllers, the proposed inverse model improves the control current’s prediction accuracy and tracks the desired damping force in real time. Moreover, the proposed inverse model and designed fuzzy controller are applied to the 1/4 vehicle suspension system simulation. The obtained results show that the optimized neural network model can be applied to a practical control. The root mean square value of body acceleration of semi-active suspension is lower than that of passive suspension under different road excitation. This method provides a foundation for the accurate modeling and semi-active control of the magneto-rheological damper.
AtkinsonCEl-AliK (1992) Some boundary value problems for the Bingham model. Journal of Non-Newtonian Fluid Mechanics41(3): 339–363.
2.
BoadaMJLCalvoJABoadaBL, et al. (2011) Modeling of a magnetorheological damper by recursive lazy learning. International Journal of Non-Linear Mechanics46(3): 479–485.
3.
BoadaMJLBoadaBLDiaV (2017) A novel inverse dynamic model for a magnetorheological damper based on network inversion. Journal of Vibration and Control24(15): 3434–3453.
4.
CaponettoRDiamanteOFargioneG, et al. (2003) A soft computing approach to fuzzy sky-hook control of semi-active suspension. IEEE Transactions on Control Systems Technology11(6): 786–798.
5.
ChengMXingJWChenZB (2018) Design, analysis, and experimental evaluation of a magnetorheological damper with meandering magnetic circuit. IEEE Transactions on Magnetics54(5): 1–10.
6.
ChoiSBLeeSKParkYP (2001) A hysteresis model for the field-dependent damping force of a magnetorheological damper. Journal of Sound and Vibration245(2): 375–383.
7.
CrosbyMJKarnoppDC (1973) The active damper: a new concept for shock and vibration control. Shock and Vibration Bulletin43: 119–133.
8.
DykeSJSpencerBFJrSainMK, et al. (1996) Modeling and control of magnetorheological dampers for seismic response reduction. Smart Materials and Structures5(5): 565–575.
9.
HongSRWereleyNMChoiYT, et al. (2008) Analytical and experimental validation of a nondimensional Bingham model for mixed-mode magnetorheological dampers. Journal of Sound and Vibration312(3): 399–417.
10.
LamAHLiaoHW (2003) Semi-active control of automotive suspension systems with magnetorheological dampers. International Journal of Vehicle Design4327(33): 125–136.
11.
LiGangLiuQJHuGL (2019a) Research review on modified and improved parametric mechanical models for magnetorheological damper. Machine Tool and Hydraulics47(11): 181–187.
12.
LiWSSunJChenW (2019b) Real-time obstacle avoidance algorithm for robots based on BP neural network. Chinese Journal of Scientific Instrument40(11):207–214.
13.
MeiCCLiuKF (1987) A Bingham-plastic model for a muddy seabed under long waves. Journal of Geophysical Research Oceans92(C13): 14581–14594.
14.
MeteredHBonelloPOyadijiSO (2010) The experimental identification of magnetorheological dampers and evaluation of their controllers. Mechanical Systems and Signal Processing24(4): 976–994.
15.
MiaoZLongHYChuJ, et al. (2018) Establishment and simulating verification of magneto-rheological damper modified Dahl model. Mechanical Engineering Automation206(1): 88–90.
16.
PangLKamathGMWereleyNM (1998) Dynamic characterization and analysis of magnetorheological damper behavior. In: Proceedings of SPIE-smart structures and materials, San Diego, USA, March 1998, pp. 284–302. San Diego: The International Society for Optical Engineering Publishing.
17.
PaulPSIasanthJAVasanthX, et al. (2015) Effect of nanoparticles on the performance of magnetorheological fluid damper during hard turning process. Friction3(4): 333–343.
18.
PhillipsRW (1969) Engineering applications of fluids with available yield stress. PhD Thesis, University of California, USA.
19.
PiatkowskiT (2014) Dahl and LuGre dynamic friction models-the analysis of selected properties. Mechanism and Machine Theory73: 91–100.
20.
QinYZhaoFWangZ, et al. (2017) Comprehensive analysis for influence of controllable damper time delay on semi-active suspension control strategies. Journal of Vibration and Acoustics139(3): 1–12.
21.
SwitonskiEMezykADudaS, et al. (2007) Prototype magnetorheological fluid damper for active vibration control system. Journal of Achievement in Materials and Manufacturing Engineering21(1): 71–74.
22.
Torres CedilloSGBonelloP (2016) Empirical identification of the inverse model of a squeeze-film damper bearing using neural networks and its application to a nonlinear inverse problem. Journal of Vibration and Control24(2): 357–378.
23.
WangDHLiaoWH (2005) Modeling and control of magnetorheological fluid dampers using neural networks. Smart Materials and Structures14(1): 111–126.
24.
WangKZhengLLiuF (2013) Model identification of MR damper based on generalized regression neural network. Automotive Engineering35(7): 619–623.
25.
WangMTongZJHouYL (2020) Study on inverse model of magnetorheological neural network based on MEA. Machine Design and Manufacturing Engineering49(1): 1–6.
26.
XiaPQ (2003) An inverse model of MR damper using optimal neural network and system identification. Journal of Sound and Vibration266(5): 1009–1023.
27.
YangG (2001) Large-scale magneto rheological fluid damper for vibration mitigation modeling testing and control. PhD Thesis, University of Notre Dame, USA.
28.
ZhengQ (2002) Simulation of vehicle semi-active suspension control based on genetic algorithm and neural networks. Journal of Hefei University of Technology25(2): 230–235.
