進階搜尋


 
系統識別號 U0026-0208202014591400
論文名稱(中文) 狀態空間模型的遞迴子空間識別法之研究
論文名稱(英文) Study on Recursive Subspace Identification of State-Space Models
校院名稱 成功大學
系所名稱(中) 土木工程學系
系所名稱(英) Department of Civil Engineering
學年度 108
學期 2
出版年 109
研究生(中文) 梁恩賜
研究生(英文) En-Tzu Liang
學號 N66074174
學位類別 碩士
語文別 英文
論文頁數 114頁
口試委員 指導教授-朱聖浩
口試委員-徐德修
口試委員-方一匡
口試委員-鍾興陽
口試委員-吳淑珍
中文關鍵字 遞迴子空間識別法  系統識別  狀態空間模型  有限元素模型 
英文關鍵字 Recursive subspace identification  System identification  State-space model  Finite element model 
學科別分類
中文摘要 本論文旨在利用多變量之遞迴子空間識別法應用到電腦輔助分析程式,藉由獲取有限元模型受到地震激勵的加速度響應做為系統識別分析所需的輸入與輸出資料,將系統的參數識別出來,並透過遞迴法不斷地更新識別的結果。利用識別出的系統參數與已知的系統質量矩陣推估系統的勁度矩陣,並將識別的結果與有限元模型計算出的結果做比較,以了解此識別法之識別準確性與適用性。
在本論文中建立了許多不同形狀及不同自由度大小的有限元模型,並施加不同最大地表加速度(PGA)與不同主要頻率(Ts)之人工地震,利用遞迴子空間識別法於地震歷時中即時並持續地進行系統識別,分析此方法對於不同型態的地震之適用性與準確性。此外,在現實的測量中,須考慮到噪訊對於加速度測量之影響,因此利用施加不同噪訊比的白噪音模擬不同程度的噪訊對於系統識別之影響,並了解遞迴子空間識別法對於噪訊的容忍度。
從本文研究的結果顯示,此系統識別法在各式各樣的建築結構中都有良好的識別結果,並且在不同型態的地震激勵下,也能有好的識別表現。此外,研究的結果也顯示,當噪訊的強度在一定的噪訊比之下,遞迴子空間識別法對於結構系統的識別仍能保持不錯的結果。電腦輔助分析程式由 朱聖浩教授研究團隊所開發,分析程式及研究成果皆為公開資源,任何人或機構皆能自由使用。
英文摘要 The purpose of this thesis is to apply the multivariable recursive subspace identification (RSI) algorithm to the computer-aided analysis program. By obtaining the acceleration responses of the finite element models during earthquake excitation as input and output data for system identification to continuously identify the system parameters through recursive algorithm. The identified system parameters and the known system mass matrix are used to estimate the stiffness matrix of the system, and compare the identification and estimation results with that computed by the finite element model to verify the accuracy and applicability of this system identification method.
In this thesis, several finite element models with different shapes and degrees of freedom are established, and artificial earthquakes with different peak ground accelerations (PGA) and different dominant periods (Ts) are applied. The RSI method is used to continuously identify the system parameters of these simulation cases during seismic excitation. In addition, in reality, the effect of noise on acceleration measurement must be considered, so white noise with different noise-to-signal ratios is applied to simulate the influence of the noise on system identification, and the tolerance of noise of RSI method can be known.
The results of this thesis indicate that this RSI method can obtain good identified results in various structural models, and under different types of earthquake excitations. In addition, it also shows that the RSI method can maintain respectable identification results when the amplitude of the noise is not too large. Note that the computer-aided analysis programs in this thesis are developed by Shen-Haw Ju’s research team, which are free to use.
論文目次 摘要 I
Abstract II
Acknowledgement III
Contents IV
List of Tables VII
List of Figures X
Chapter 1 Introduction 1
1.1 Background and research purposes 1
1.2 Literature Review 2
1.2.1 Research correlated to system identification 2
1.2.2 Research correlated to model order reduction and model correction method 4
1.2.3 Research correlated to control algorithms of ATMD 5
1.3 Overview 6
Chapter 2 Research Theories and Methods 7
2.1 State-Space Modeling of a Structure 7
2.1.1 Continuous-time state-space model 7
2.1.2 Discrete-time state-space model 8
2.2 Subspace Identification for Modal Analysis 9
2.2.1 Data organization 10
2.2.2 Estimation of the extended observability matrix 12
2.3 Recursive Subspace Identification 17
2.4 Model order reduction of finite element models 22
2.4.1 Guyan reduction (Static condensation) 22
2.4.2 Improved Reduction System (IRS) method 23
2.5 Suggestion for User-defined Parameters 25
Chapter 3 Simulation Programs and Operating Procedures 27
3.1 Relevant Programs for Finite Element Models 27
3.1.1 Description of relevant programs 27
3.1.2 Operating procedures for finite element model 28
3.2 Generation of Artificial Earthquakes 38
3.3 Recursive Subspace System Identification Procedure 41
3.3.1 Operating procedure of RSI programs 43
Chapter 4 Simulation Study 44
4.1 System Identification of Different Models Under Same Artificial Earthquake Excitation 44
4.1.1 Model description 44
4.1.2 System identification results 45
4.2.1 The Model Under Artificial Earthquakes with Different Ts 68
4.2.2 The Model Under Artificial Earthquakes with Different PGA 70
4.3 Modal Identification of Models with White Noise 72
4.4 System Identification of Several Special Models 86
Chapter 5 Result and Discussion 96
5.1 Discussion of General Shape Model Cases 96
5.2 Discussion of Models Under Different Earthquakes 98
5.3 Discussion of Models Consider Measured White Noise 99
5.4 Discussion of Several Special Model Cases 100
Chapter 6 Conclusions and Future Work 102
6.1 Conclusions 102
6.2 Future Work 104
6.2.1 Equations of motion of an active control system 104
6.2.2 LQR control method 105
Reference 107
Appendix A 111

參考文獻 Alavinasab, A., Moharrami, H. and Khajepour, A., Active control of structures using energy-based LQR method. Computer-Aided Civil and Infrastructure Engineering, Vol, 21, No. 8, pp. 605-611, 2006.
Abazarsa, F., Ghahari, S. F., Nateghi, F. and Taciroglu, E., Response-only modal identification of structures using limited sensors. Structural Control and Health Monitoring, Vol. 20, No. 6, pp. 987-1006, 2013.
Catbas, F.N., Ciloglu, S.K., Hasancebi, O., Grimmelsman, K. and Aktan, A. E., Limitations in structural identification of large constructed structures. Journal of Structural Engineering, Vol. 133, No. 8, pp. 1051-1066, 2007.
Chen, J.D. and Loh, C.H., Tracking modal parameters of building structures from experimental studies and earthquake response measurements. Structural Health Monitoring - An International Journal, Vol. 16, No. 5, pp. 551-567, 2017.
Chen, J.D., Application of Online Recursive Subspace Identification on Structural Stiffness Assessment and Quantification. Master Thesis, National Taiwan University, 2017.
De Cock, K., Mercere, G. and De Moor, B., Recursive subspace identification for in-flight modal analysis of airplanes. In Proceedings of the International Conference on Noise and Vibration Engineering, Leuven, Belgium, 2006.
Floden, O., Persson, K. and Sandberg, G., Reduction methods for the dynamic analysis of substructure models of lightweight building structures. Computers and Structures, Vol. 138, pp. 49-61, 2014.
Guyan G.J., Reduction of Stiffness and Mass Matrices. American Institute of Aeronatics and Astronautics Journal, Vol. 3, No. 2, pp. 380, 1965.
Guclu, R. and Yazici, H., Vibration control of a structure with ATMD against earthquake using fuzzy logic controllers. Journal of Sound and Vibration, Vol. 318, No. 1-2, pp. 36-49, 2008.
Heidari, AH., Etedali, S. and Javaheri-Tafti, MR., A hybrid LQR-PID control design for seismic control of buildings equipped with ATMD. Frontiers of Structural and Civil Engineering, Vol. 12, No. 1, pp. 44-57, 2018.
IBC. International Building Code 2006, International Code Council: Birmingham, AL, USA, 2006.
Juang, J. N., Applied system identification, Prentice Hall, Englewood Cliffs, 1994.
Kameyama, K., Ohsumi, A., Matsuura, Y. and Sawada, K., Recursive 4SID-based identification algorithm with fixed input-output data size. International Journal of Innovative Computing Information and Control, Vol. 1, No. 1, pp. 17-33, 2005.
Koutsovasilis, P. and Beitelschmidt, M., Model order reduction of finite element models: improved component mode synthesis. Mathematical and Computer Modelling of Dynamical Systems, Vol. 16, No. 1, pp. 57-73, 2010.
Lovera, M., Gustafsson, T. and Verhaegen, M, Recursive subspace identification of linear and non-linear Wiener state-space models. Automatica, Vol. 36, No. 11, pp. 1639-1650, 2000.
MIT. SIMQKE: A Program for Artificial Motion Generation: User’s Manual and Documentation; M.I.T. Department of Civil Engineering: Cambridge, MA, USA, 1976.
Mercere, G., Bako, L. and Lecoeuche, S., Propagator-based methods for recursive subspace model identification. Signal Processing, Vol. 88, No. 3, pp. 468-491, 2008.
Nazarimofrad, E. and Zahrai, S. M., Fuzzy control of asymmetric plan buildings with active tuned mass damper considering soil-structure interaction. Soil Dynamics and Earthquake Engineering, Vol. 115, pp. 838-852, 2018.
O’Callahan, J.C., A procedure for an Improved Reduced System (IRS) Model. Proceedings of the 7th International Modal Analysis Conference, Las Vegas, pp. 17-21, 1989
Peeters, B. and de Roeck, G., Reference-based stochastic subspace identification for output-only modal analysis. Mechanical Systems and Signal Processing, Vol. 13, No. 16, pp. 855-878, 1999.
Pourzeynali, S., Lavasani, H. H. and Modarayi, A. H., Active control of high rise building structures using fuzzy logic and genetic algorithms. Engineering Structures, Vol. 29, No. 3, pp. 346-357, 2007.
Reynders, E., and De Roeck, G., Reference-based combined deterministic-stochastic subspace identification for experimental and operational modal analysis. Mechanical Systems and Signal Processing, Vol. 22, No. 3, pp. 617-637, 2008.
Salvini, P. and Vivio, F., Dynamic reduction strategies to extend modal analysis approach at higher frequencies. Finite Elements in Analysis and Design, Vol. 43, No. 11-12, pp. 931-940, 2007.
Su, W.C., Huang, C.S., Lien, H.C. and Le, Q.T., Identifying the stiffness parameters of a structure using a subspace approach and the Gram-Schmidt process in a wavelet domain. Advances in Mechanical Engineering, Vol. 9, No. 7, pp. 1-13, 2017.
Tamaoki, M., Akizuki, K. and Oura, K., Order and parameter estimation of time-varying system by subspace method. Electrical Engineering in Japan, Vol. 157, No. 2, pp. 57-64, 2006.
Verhaegen, M. and Deprettere, E., A fast, recursive MIMO state space model identification algorithm. Proceedings of the 30th Conference on Decision and Control, Brighton, England, 1991.
Verhaegen, M., Identification of the deterministic part of MIMO state space models given in innovations form from input-output data. Automatica, Vol. 30, No. 1, pp. 61-74, 1994.
Weng, J.H., and Loh, C.H., Recursive subspace identification for on-line tracking of structural modal parameter. Mechanical Systems and Signal Processing, Vol. 25, No. 8, pp. 2923-2937, 2011.
Yang, Y. B. and Chen, Y. J., A new direct method for updating structural models based on measured modal data. Engineering Structures, Vol. 31, No. 1, pp. 32-42, 2009.
Yuen, K. V., Efficient Model Correction Method with Modal Measurement. Journal of Engineering Mechanics, Vol. 136, No. 1, pp. 91-99, 2010.
Yang, J., Ouyang, H. J. and Zhang, J. F., A new method of updating mass and stiffness matrices simultaneously with no spillover. Journal of Vibration and Control, Vol. 22, No. 5, pp. 1181-1189, 2014.
論文全文使用權限
  • 同意授權校內瀏覽/列印電子全文服務,於2020-08-14起公開。
  • 同意授權校外瀏覽/列印電子全文服務,於2020-08-14起公開。


  • 如您有疑問,請聯絡圖書館
    聯絡電話:(06)2757575#65773
    聯絡E-mail:etds@email.ncku.edu.tw