Continuous-time frequency domain subspace system identification

Abstract In this paper we present a new subspace identification algorithm for the identification of multi-input multi-output linear time-invariant continuous-time systems from measured frequency response data. We show how the conditioning of the data-matrices in the algorithm can be improved by making use of recursions derived from the Forsythe polynomials. The asymptotic properties are analyzed and it is shown that, when the error distribution on the measurements is given, the algorithm can be made asymptotically unbiased through the introd uetion of a weight ing matrix.

[1]  Wallace E. Larimore,et al.  Canonical variate analysis in identification, filtering, and adaptive control , 1990, 29th IEEE Conference on Decision and Control.

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

[3]  Bart De Moor,et al.  Numerical algorithms for state space subspace system identification , 1993 .

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

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

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

[7]  R. Pintelon,et al.  On the use of orthogonal polynomials in high order frequency domain system identification and its application to modal parameter estimation , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.

[8]  Joe Brewer,et al.  Kronecker products and matrix calculus in system theory , 1978 .

[9]  Bart De Moor,et al.  N4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems , 1994, Autom..

[10]  Mats Viberg,et al.  Subspace Methods in System Identification , 1994 .

[11]  T. McKelvey Identification of State-Space Models from Time and Frequency Data , 1995 .

[12]  B. Moor,et al.  Subspace identification for linear systems , 1996 .

[13]  Bart De Moor,et al.  The singular value decomposition and long and short spaces of noisy matrices , 1993, IEEE Trans. Signal Process..

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

[15]  Michel Verhaegen,et al.  Identification of the deterministic part of MIMO state space models given in innovations form from input-output data , 1994, Autom..

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

[17]  P. Overschee Subspace Identification: Theory, Implementation, Application , 1995 .

[18]  H. Akçay,et al.  An efficient frequency domain state-space identification algorithm , 1994, Proceedings of 1994 33rd IEEE Conference on Decision and Control.