Subspace-based system identification: weighting and pre-filtering of instruments

Authors:
Tony Gustafsson
Affiliations:
Department of Signals and Systems, Chalmers University of Technology, S-412 96 Göteborg, Sweden
Venue:
Automatica (Journal of IFAC)
Year:
2002

Citing 7
Cited 2

Identification of the deterministic part of MIMO state space models given in innovations form from input-output data

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Subspace-based methods for the identification of linear time-invariant systems

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On Consistency of Subspace Methods for System Identification

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Analysis of the asymptotic properties of the MOESP type of subspace algorithms

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A novel subspace identification approach with enforced causal models

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Abstract

Subspace-based system identification is typically based on an estimate of the extended observability matrix. It is thus of great interest to investigate, and also optimize, the estimate of the observability matrix. Of special interest in this paper is the fact that the influence of certain weighting matrices is an unresolved issue in the literature on subspace identification. Here, an asymptotic analysis of the estimated observability matrix is presented. The main result of the analysis is that novel weighting matrices and pre-filters of instrumental variables are derived.