部分加速度测量下结构恢复力与质量非参数化识别方法  

Nonparametric restoring force and mass identification approach usinglimited acceleration measurement

作　　者：邓百川 郭玉荣[1] 许斌[2,3] DENG Baichuan;GUO Yurong;XU Bin(College of Civil Engineering,Hunan University,Changsha 410082,China;College of Civil Engineering,Huaqiao University,Xiamen 361021,China;Key Laboratory of Intelligent Infrastructure and Monitoring of Fujian Province,Huaqiao University,Xiamen 361021,China)

机构地区：[1]湖南大学土木工程学院,长沙410082 [2]华侨大学土木工程学院,福建厦门361021 [3]华侨大学福建省智慧基础设施与监测重点实验室,福建厦门361021

出　　处：《振动与冲击》2020年第8期23-32,共10页Journal of Vibration and Shock

基　　金：国家自然科学基金(50978092;51878259)。

摘　　要：传统的基于扩展卡尔曼滤波方法的结构非线性行为识别方法往往要求结构质量以及结构非线性恢复力的参数化模型已知。该研究为解决非线性结构质量,结构参数,非线性恢复力的识别问题,提出了一种两阶段识别方法;为提高计算效率采用遗忘因子扩展卡尔曼滤波算法结合等效线性模型实现结构非线性位置的定位,随后采用无迹卡尔曼滤波算法与恢复力的二重切比雪夫多项式非参数化模型识别结构参数,质量与恢复力。在对一个含形状记忆合金(SMA)阻尼器的多自由度体系的数值模型进行了数值模拟验证的基础上,设计了一个含SMA阻尼器的四自由度框架开展动力试验,验证了所提出方法对结构质量以及恢复力的识别效果。Identification of nonlinear structures using traditional extended Kalman filter(EKF)usually requires structure mass and the parametric model of restoring force are known.In this paper,to identify structural parameters,mass and nonlinear restoring force(NRF),a two-steps identification approach using limited acceleration response and nonparametric model was provided.Firstly,memory fading extended Kalman filter(MF-EKF)combining with the equivalent linear model was provided to identify the location of nonlinearities in order to improve calculation efficiency.Secondly,the unscented Kalman filter(UKF)combining with the double Chebyshev polynomial model was provided to identify the mass,structural parameters(stiffness,damping)and NRF.The feasibility of the proposed approach was illustrated via numerical simulation with a multi-degree-of-freedom(MDOF)structure equipped with a Shape Memory Alloy(SMA)damper.Finally,a dynamic experiment with a four-DOFs frame equipped with an SMA damper was designed to illustrate the feasibility of the method.

关 键 词：扩展卡尔曼滤波方法(EKF) 遗忘因子(MF) 无迹卡尔曼滤波(UKF) 二重切比雪夫多项式模型 SMA阻尼器 非线性恢复力 质量识别 

分 类 号：TH212[机械工程—机械制造及自动化] TH213.3