期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

基于状态空间和NR-LMS的结构参数辨识方法 被引量：1

State space theory and NR-LMS algorithm based method for structural dynamics parameter identification
下载PDF
导出
摘要 提出一种基于系统状态空间模型和归一化鲁棒最小均方根(NR-LMS,Normalized Robust Least Mean Square)理论的动力学结构参数辨识方法.利用系统的输入-输出数据建立其Hankel-Toeplitz模型,利用NR-LMS算法得到该模型参数的估计并求得系统的Hankel矩阵,对Hankel矩阵进行奇异值分解即可确定系统的阶次,进而确定系统状态空间模型的参数.仿真研究和实验结果表明,此方法可以准确、快速地提取出结构的参数,且抗噪能力较强. A parameter identification method for structural dynamics system based on state space （SS）theory and normalized robust least mean square （NR-LMS） algorithm was proposed- By using this method,the identified dynamic system＇ s input and output data were used to build its Hankel-Toeplitz model based onthe state space theory- Iterative NR-LMS algorithm was applied to achieve parameters＇ estimates and Hankelmatrix for this model- Singular value decomposition （SVD） method to Hankel matrix was employed for quanti-fying the order of this dynamic system- Modal parameters and the state space model＇ s parameters also couldbe achieved from the Hankel matrix by certain calculation- A simulation of 3-DOF（ degree of freedom） spring-mass system was employed to validate this method and experiment of identifying cantilever＇ s parameters wasstudied- The results demonstrate this method can identify structural parameters accurately and quickly-
作者 王云峰 程伟 陈江攀
机构地区 北京航空航天大学航空科学与工程学院
出处 《北京航空航天大学学报》 EI CAS CSCD 北大核心 2014年第4期517-522,共6页 Journal of Beijing University of Aeronautics and Astronautics
关键词 状态空间理论 Hankel-Toeplitz模型 参数辨识 HANKEL矩阵 归一化鲁棒 最小均方根(NR-LMS)方法 state space theory Hankel-Toeplitz model parameter identification Hankel matrix normalized robust least mean square （NR-LMS） algorithm
分类号 TB123 [理学—工程力学]
  • 相关文献

参考文献12

  • 1Gawronski W K. Advanced structural dynamics and active con- trol of structures [ M ]. New York: Springer-Verlag, Inc, 2004: 219 - 226.
  • 2Katayama T. Subspace methods for system identification [ M ]. London : Springer-Verlag, Inc, 2005 : 33 - 39.
  • 3Yeung W T,Smith J W. Damage detection in bridges using neu- ral networks for pattern recognition of vibration signstures [ J ]. Engineering Structures ,2005,27 ( 5 ) :685 - 698.
  • 4Kof C G,Hong B ,Lian C Y. Substructural and progressive struc- tural identification methods [ J ]. Engineering Structures, 2003, 25(12) :1551 - 1563.
  • 5付志超,程伟,徐成.基于LS-SVM的模态参数识别方法[J].航空学报,2009,30(11):2087-2092. 被引量：4
  • 6Juang J N, Pappa R S. An eigensystem realization algorithm (ERA) for model parameter identification and model reduction [ J]. Journal of Guidance, Control and Dynamics, 1985,8 ( 5 ) : 620 - 627.
  • 7Cao Y, Chen X B. State space system identification of 3-degree- of-freedom piezo-actuator-driven stages with unknown configura- tion [J]. Actuators,2013,2(1) :1 - 18.
  • 8De Callafon R A, Moaveni B. General realization algorithm for modal identification of linear dynamic systems [ J ]. Journal of Engineering Mechanics ,2008,134 ( 9 ) :712 - 722.
  • 9Borjas S D M, Garcia C. Subspace identification for industrial process [ J ]. Tema Tend Mat Appl Comput, 2011,12 ( 3 ) : 183 - 194.
  • 10Juang J N, Phan M, Horta L G, et al. Identification of observer/ Kalman filter Marknv parameters:theory and experiments [ J]. Journal of Guidance, Control, and Dynamics, 1993, 16 ( 2 ) : 320 - 329.

二级参考文献17

  • 1Kitagawa G. Monte Carlo filter and smoother for non Gaussian nonlinear state space models[J]. Journal of Computational and Graphical Statistics, 1996, 5 (1) : 1 -25.
  • 2Ko J M, Sun Z G, Ni Y Q. Multi-stage identification scheme for detecting damage in cable stayed Kap Shui Mun Bridge[J]. Engineering Structures, 2002, 24 (7): 857- 868.
  • 3Yeung W T, Smith J W. Damage detection in bridges using neural networks for pattern recognition of vibration signatures[J]. Engineering Structures, 2005, 27(5): 685- 698.
  • 4Koh C G, Hong B, Liaw C Y. Substructural and progressive structural identification methods [ J]. Engineering Structures, 2003, 25(12):1551-1563.
  • 5Yang J N, Lei Y, Pan S W, et al. System identification of linear structures based on Hilbert Huang spectral analysis Part 1: normal modes[J]. Earthquake Engineering and Structural Dynamics, 2003, 32(9): 1443- 1467.
  • 6Ong K C G, Wang Z, Maalej M. Adaptive magnitude spectrum algorithm for Hilbert Huang transform based frequency identification[J]. Engineering Structures, 2008, 30 (1): 33-41.
  • 7Pakrashi B, Basu B, Connor A O. Structural damage detection and calibration using a wavelet-kurtosis technique [J]. Engineering Structures, 2007, 29(9):2097-2108.
  • 8Law S S, Li X Y, Zhu X Q, et al. Structural damage detection from wavelet packet sensitivity[J]. Engineering Structures, 2005, 27(9): 1339-1348.
  • 9Kim H, Melhem H. Damage detection of structures by wavelet analysis[J]. Engineering Structures, 2004, 26 (3) : 347-362.
  • 10Zhang B, Shi Z K, I.i J J. Flight flutter modal parameters identification with atmospheric turbulence excitation based on wavelet transformation[J]. Chinese Journal of Aero nautics, 2007, 20(5):394-401.

共引文献3

同被引文献2

引证文献1

二级引证文献4

北京航空航天大学学报
2014年 第4期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部