Elsevier

Automatica

Volume 31, Issue 12, December 1995, Pages 1835-1851
Automatica

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

https://doi.org/10.1016/0005-1098(95)00107-5Get rights and content

Abstract

Subspace-based methods for system identification have attracted much attention during the past few years. This interest is due to the ability of providing accurate state-space models for multivariable linear systems directly from input-output data. The methods have their origin in classical state-space realization theory as developed in the 1960s. The main computational tools are the QR and the singular-value decompositions. Here, an overview of existing subspace-based techniques for system identification is given. The methods are grouped into the classes of realization-based and direct techniques. Similarities between different algorithms are pointed out, and their applicability is commented upon. We also discuss some recent ideas for improving and extending the methods. A simulation example is included for comparing different algorithms. The subspace-based approach is found to perform competitive with respect to prediction-error methods, provided the system is properly excited.

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    The original version of this paper was presented as an invited survey paper at the 10th IFAC Symposium on System Identification, which was held in Copenhagen, Denmark during 4–6 July 1994. The Published Proceedings of this IFAC meeting may be ordered from: Elsevier Science Limited, The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, U.K. This paper was recommended for publication in revised form by Guest Editors Torsten Söderström and Karl Johan Åström.

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