Modeling and Vibration Analysis of Semi-active Seat Suspension with Magnetorheological Damper
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摘要:
磁流变阻尼器力学模型及控制电流逆模型对半主动控制系统的控制精度具有重要影响. 采用正弦及余弦型魔术公式，基于骨架曲线与滞回分离的建模方法，建立改进的磁流变阻尼器动态阻尼力模型；采用基于Sobol序列的差分-禁忌混合优化算法对阻尼力模型进行参数识别，构建包含激励特性及控制电流参数的通用数学模型；在试验测试及正向模型基础上，利用自适应神经模糊系统建立阻尼器控制电流逆模型. 研究结果表明：本文建立的正逆模型均能够有效表征磁流变阻尼器的非线性行为及滞回特性；改进魔术公式模型在不同激励特性及电流工况下的平均百分比误差在3.4%附近变化；逆向动力学模型计算的控制电流误差均方根值为0.0869～0.1171 A；经过控制电流逆模型与阻尼器正向模型串联模型计算的预测阻尼力误差均方根值为阻尼器最大阻尼力的5.6%；通过试验测试与仿真结果对比，验证了本文提出的阻尼器数学模型具有较好的精度和适用性，能够改善座椅悬架系统振动传递特性.
Abstract:The magnetorheological (MR) damper has attracted an increasing amount of attention in the field of vibration control because of its excellent adjustable damping performance. A modified model based on the modeling method of skeleton curve and hysteresis separation is proposed with sine and cosine magic formulas of MR damper in this study. The sobol sequence-based differential-tabu hybrid algorithm (SS-DTHA) is used to identify the parameters of the damping force model, and a general mathematical model including excitation characteristics and control current parameters is established. On the basis of test data and forward damper force model, the inverse model of MR damper control current is established by using adaptive-network-based fuzzy inference systems (ANFIS). The results show that the forward and inverse models established in this paper can better characterize the nonlinear behavior and hysteretic characteristics of the magnetorheological damper. The average percentage error of the improved magic formula model varies around 3.4% under different excitation characteristics and current conditions. The root means square (RMS) error of control current calculated by inverse dynamics model is 0.0869−0.1171 A. The RMS of error between the predicted damping force calculated by the control current inverse model and the damper forward model in series is 5.6% of the maximum damping force of the damper. Through the comparison of test data and simulation results, it is proved that the mathematical model of damper proposed in this paper has good accuracy and applicability, and can improve the vibration transmission characteristics of seat suspension system.
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Key words:
- automotive engineering /
- seat suspension /
- MR damper /
- vibration control /
- mathematical models
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图 6 不同模型计算值与试验数据对比
Figure 6. Comparison of damping forces calculated by different models
图 7 不同工况下模型计算值与试验数据对比
Figure 7. Comparison of damping forces calculated by the model under different working conditions
图 8 参数Bp、Bh、c随激励及电流的变化规律
Figure 8. Variation of parameter Bp，Bh，c with excitation and current conditions
图 9 通用模型辨识结果与试验数据对比
Figure 9. Comparison of damping forces between generalized model and test data
图 14 半主动与被动座椅悬架位移传递率对比
Figure 14. Comparison of transmissibility between semi-active and passive seat suspension
图 15 包含时滞特性的半主动座椅悬架系统框图
Figure 15. Application of time delay of the whole semi-active control system
图 16 不同时间延迟对座椅悬架位移传递率影响对比
Figure 16. Comparison of the time delay effect to seat transmissibility
表 1 不同工况模型平均百分比误差
Table 1. Comparison of average percentage error of models under different working conditions
电流/A 幅值 5 mm 幅值 15 mm 3 Hz 4 Hz 3 Hz 4 Hz 0 0.0447 0.0355 0.0457 0.0452 0.2 0.0474 0.0355 0.0289 0.0396 0.4 0.0316 0.0321 0.0302 0.0319 0.6 0.0310 0.0322 0.0269 0.0284 0.8 0.0344 0.0349 0.0305 0.0315 1.0 0.0308 0.0359 0.0282 0.0273 
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表 2 滞回模型参数辨识结果
Table 2. Parameter identification of hysteretic model
变量 辨识结果 ${F}_{{\rm{max}}}$ $\left(-9.36{I}^{2} + 123.97I + 23.05\right)\left(4.29{ {v}^{0.19}_{{\rm{m}}} }\right)$ ${\dot{x} }_{{\rm{center}}}$ $\left(-3.47I + 1.91\right)(-2.35{\rm{ln} }\;{v}_{ {\rm{m} } } + 9.21)$ ${B}_{{\rm{p}}}$ $1.97{v}^{-1.09}_{{\rm{m}}}$ ${B}_{{\rm{h}}}$ $1.01{v}_{{\rm{m}}}^{-0.86}$ $ c $ $0.32{ {v}_{ {\rm{m} } } ^{-1.01}}$ Cp 1.32 Dp 0.70 Ep −7.64 Ch 1.10 Dh 0.42 Eh −11.50 f0 −80.34
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