Structure analysis of matrix fraction descriptions for LTI Systems

This paper investigates representation of a multivariable linear time-invariant system by matrix fraction descriptions (MFD). The main theoretical results in our recent paper [1] have resolved several long-standing issues of MFD models, including uniqueness of MFD pairs fA(z) B(z)g for a given impulse response of the system, complete characterization of the orders of all possible MFDs of a given system, testing criteria for determining whether a matrix pair is an MFD of the system, and algorithms for deriving all MFD representations from certain Toeplitz matrices constructed from impulse responses. For real-time implementation, identification algorithms are introduced that estimate all MFDs of a given system from its input-output data. The results are then extended to cover ARMAX systems. This conference paper aims to reach a broader audience and stimulate potential applications of the methodology and algorithms by highlighting the main results, elaborating key ideas with more discussions, extending simulation studies, and including more examples.

[1]  Petre Stoica,et al.  Generalized Yule-Walker equations and testing the orders of multivariate time series , 1983 .

[2]  D. Mitchell Wilkes,et al.  Robust and accurate ARX and ARMA model order estimation of non-Gaussian processes , 2002, IEEE Trans. Signal Process..

[3]  Han-Fu Chen,et al.  Hankel matrices for system identification , 2014 .

[4]  Gilead Tadmor,et al.  Identifiability and persistent excitation in full matrix fraction parameter estimation , 1997, Autom..

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

[6]  Han-Fu Chen,et al.  Identification and Stochastic Adaptive Control , 1991 .

[7]  Han-Fu Chen,et al.  Recursive Identification of MIMO Wiener Systems , 2013, IEEE Transactions on Automatic Control.

[8]  J. Rissanen,et al.  Modeling By Shortest Data Description* , 1978, Autom..

[9]  Tong Zhou,et al.  Frequency response estimation for NCFs of an MIMO plant from closed-loop time-domain experimental data , 2006, IEEE Transactions on Automatic Control.

[10]  B. G. Quinn,et al.  The determination of the order of an autoregression , 1979 .

[11]  Brahim Aksasse,et al.  A rank test based approach to order estimation. I. 2-D AR models application , 1999, IEEE Trans. Signal Process..

[12]  H. Akaike A new look at the statistical model identification , 1974 .

[13]  Wen-Xiao Zhao,et al.  New Method of Order Estimation for ARMA/ARMAX Processes , 2010, SIAM J. Control. Optim..

[14]  G. Schwarz Estimating the Dimension of a Model , 1978 .

[15]  D. Mitchell Wilkes,et al.  ARMA model order estimation based on the eigenvalues of the covariance matrix , 1993, IEEE Trans. Signal Process..

[16]  Tong Zhou,et al.  Nonparametric estimation for normalized coprime factors of a MIMO system , 2005, Autom..

[17]  Le Yi Wang,et al.  Characterization and Identification of Matrix Fraction Descriptions for LTI Systems , 2014, SIAM J. Control. Optim..

[18]  J. Cadzow,et al.  Spectral estimation: An overdetermined rational model equation approach , 1982, Proceedings of the IEEE.

[19]  E. Hannan The identification of vector mixed autoregressive-moving average system , 1969 .