磁流变阻尼器动力性能测试与建模

发布时间：2018-03-01 18:52

本文关键词： 磁流变阻尼器动力性能测试双曲正切滞回模型 BP神经网络模型　出处：《振动.测试与诊断》2017年03期 　论文类型：期刊论文

【摘要】：建立磁流变阻尼器精确的力学模型是进行磁流变阻尼减振结构反应分析与设计并取得良好振动控制效果的一个重要前提。首先,对一个最大出力为10kN的磁流变阻尼器进行动力性能测试;其次,基于试验结果分别建立该阻尼器的参数化与非参数化动力学模型,并对所建立模型的有效性进行验证;最后,对两种不同建模方式的结果进行对比分析。结果表明,建立的参数化模型——双曲正切滞回模型能够有效地描述磁流变阻尼器的动力特性;非参数化模型——反向传播(back propagation,简称BP)神经网络正向、逆向力学模型具有良好的训练样本拟合度、泛化能力和抗噪性能;在试验数据拟合度上,BP神经网络模型要好于双曲正切滞回模型,但后者阻尼力表达式形式简单,更易于程序化。
[Abstract]:The establishment of an accurate mechanical model for Mr dampers is an important prerequisite for the analysis and design of the response of Mr dampers and for achieving good vibration control effects. The dynamic performance of a magneto-rheological damper with maximum output force of 10kN is tested. Secondly, the parameterized and non-parameterized dynamic models of the damper are established based on the experimental results, and the validity of the proposed model is verified. The results of two different modeling methods are compared and analyzed. The results show that the parametric model-hyperbolic tangent hysteretic model can effectively describe the dynamic characteristics of Mr dampers. The non-parameterized model, back-propagation propagation (BPNN) neural network, and the reverse mechanics model have good training sample fit, generalization ability and anti-noise performance. The BP neural network model is better than the hyperbolic tangent hysteretic model in the fitting degree of the test data, but the damping force expression of the latter is simple and easy to program.
【作者单位】：华侨大学土木工程学院;福建省结构工程与防灾重点实验室;
【基金】：国家自然科学基金资助项目(51409107,51608212) 中国博士后科学基金资助项目(2014M551832) 福建省自然科学基金资助项目(2015J01211)
【分类号】：TB535.1

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