Paper Info
Title
Subspace-based methods for the identification of linear time-invariant systems
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.
Year
DOI
Venue
1995
10.1016/0005-1098(95)00107-5
Automatica
Keywords
Field
DocType
subspace-based method,linear time-invariant system,linear system,system identification,singular value decomposition,state space,input output,instrumental variable,state space model,parameter estimation,linear time invariant
LTI system theory,Multivariable calculus,Subspace topology,Linear system,Control theory,Estimation theory,System identification,Mathematics,Statistical analysis
Journal
Volume
Issue
ISSN
31
12
0005-1098
Citations 
PageRank 
References 
80
17.80
16
Authors
1
Name
Order
Citations
PageRank
Mats Viberg11043126.67