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。
Establishing precise mechanical models of magnetorheological dampers is an important prerequisite not only for the response analysis and design of structures with the dampers,but also for obtaining good vibration control effect.In this paper,the dynamic performance test of a magnetorheological damper with the nominated maximum damping force of 10 kN is first carried out.Based on the test results,parametric and non-parametric dynamic models of the damper are developed and the effectiveness of the proposed models is verified.Finally,the results of the two different modeling approaches are compared.Results show that the formulated hyperbolic tangent hysteresis model(parametric model)could well describe the dynamic behavior of the magnetorheological damper.Besides,both the forward and reverse back propagation(BP)neural network models(non-parametric models)have good performance in fitting training data,generalization ability and noise immunity.Furthermore,the BP neural network models fit with a higher accuracy than the hyperbolic tangent hysteresis models,while the later enjoy a simpler expression of the damping force which is relatively easier to implement in software.
出处
《振动．测试与诊断》
EI
CSCD
北大核心
2017年第3期553-559,共7页
Journal of Vibration,Measurement & Diagnosis
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)
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
magnetorheological damper
dynamic performance test
hyperbolic tangent hysteresis model
BP neural network model
作者简介
梅真,男,1983年8月生,讲师.主要研究方向为结构振动控制.曾发表《Experimental and analytical studies on stochastic seismicresponsec ontrol of structures with MRdampers》（《Earthquakes and Structures》2013,Vol.5,No.4）等论文.E-mail：meizhen83@163.com
