Structural system identification - A study of different algorithms

This paper uses Observability Range Space Extraction (ORSE) identification algorithm as a main thread to study various identification algorithms for structural systems. These algorithms include: Eigensystem Realization Algorithm (ERA), Q-Markov Covariance Equivalent Realization (Q-Markov Cover or QMC) algorithm, Least Squares algorithm for AutoRegressive eXogeneous model identification (LSARX), and ordinary Multi-input and multi-output Output Error State space (MOESP) model identificaiton algorithm. ERA, QMC, and ORSE algorithms have been successfully applied to structure system identifications. They have demonstrated robust noise suppression property and well behaved numerical properties. Comparison of these three algorithms shows that the ORSE algorithm is an extension of the ERA and QMC algorithms for general input and output data. LSARX algorithm was integrated with ERA algorithm for structural system identification. This paper investigates relationships between LSARX and ORSE algorithms and shows some potential disadvantages of using LSARX algorithm for high dimensional structural systems. Although ordinary MOESP algorithm has not been applied to structural systems, the algorithm and ORSE algorithm are very similar and differ only in computational implementation. Their similarities and differences are discussed. Finally, bias problems of the ORSE algorithm is studied and an unbiased version of the algorithm is derived.

[2]  S. Batill,et al.  A recursive algorithm for discrete time domain parameter identification , 1990 .

[3]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[4]  M. Phan,et al.  Identification of observer/Kalman filter Markov parameters: Theory and experiments , 1993 .

[5]  J. Juang,et al.  Effects of Noise on Modal Parameters Identified by the Eigensystem Realization Algorithm , 1986 .

[6]  Jer-Nan Juang,et al.  An Eigensystem Realization Algorithm in Frequency Domain for Modal Parameter Identification , 1986 .

[7]  Patrick Dewilde,et al.  Subspace model identification Part 1. The output-error state-space model identification class of algorithms , 1992 .

[8]  Β. L. HO,et al.  Editorial: Effective construction of linear state-variable models from input/output functions , 1966 .

[9]  K. Liu.  Identification of multi-input multi-output systems by observability range space extraction , 1992, [1992] Proceedings of the 31st IEEE Conference on Decision and Control.

[10]  Richard W. Longman,et al.  Comparison of several system identification methods for flexible structures , 1991 .

[11]  M. Moonen,et al.  On- and off-line identification of linear state-space models , 1989 .

[12]  Robert N. Jacques,et al.  Multivariable model identification from frequency response data , 1993, Proceedings of 32nd IEEE Conference on Decision and Control.

[13]  Marc Moonen,et al.  A geometrical approach for the identification of state space models with singular value decomposition , 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.

[14]  Jer-Nan Juang,et al.  An eigensystem realization algorithm for modal parameter identification and model reduction. [control systems design for large space structures] , 1985 .

[15]  S. M. Batill,et al.  Structural Identification Using Order Overspecified Time-Series Models , 1992 .

[16]  R. S. Pappa,et al.  ERA Modal Identification Experiences with Mini-Mast. , 1990 .

[17]  D. Williamson.  Digital control and implementation : finite wordlength considerations , 1991 .

[18]  Bhaskar D. Rao,et al.  Model based processing of signals: a state space approach , 1992, Proc. IEEE.

[19]  M. Verhaegen.  Subspace model identification Part 2. Analysis of the elementary output-error state-space model identification algorithm , 1992 .

[20]  David W. Miller,et al.  System Identification by the ORSE technique with a Finite Number of Data Samples , 1993 .

[21]  U. B. Desai,et al.  A Generalized Approach to q-Markov Covariance Equivalent Realizations for Discrete Systems , 1987, 1987 American Control Conference.

[22]  Sun-Yuan Kung,et al.  A new identification and model reduction algorithm via singular value decomposition , 1978 .

[23]  M. Phan,et al.  Integrated system identification and state estimation for control offlexible space structures , 1992 .

[24]  Brian D. O. Anderson,et al.  Weighted q-Markov covariance equivalent realizations , 1989 .

[25]  Michel Verhaegen,et al.  A Novel Non-Iterative Mimo State Space Model Identification Technique , 1991 .

[26]  Robert N. Jacques,et al.  Frequency domain structural system identification by observability range space extraction , 1994, Proceedings of 1994 American Control Conference - ACC '94.

[27]  R. Skelton.  Dynamic Systems Control: Linear Systems Analysis and Synthesis , 1988 .

[28]  R. Skelton,et al.  Q-Markov covariance equivalent realization and its application to flexible structure identification , 1993 .

[29]  R. Skelton,et al.  Modeling Hubble Space Telescope Flight Data by Q-Markov Cover Identification , 1992, 1992 American Control Conference.

[30]  B. Moor,et al.  A geometrical strategy for the identification of state space models of linear multivariable systems with singular value decomposition , 1987 .

[31]  David W. Miller,et al.  Time Domain State Space Identification of Structural Systems , 1995 .