Open AccessClosed‐loop subspace identification methods: an overview
Author(s) -
Veen Gijs,
Wingerden JanWillem,
Bergamasco Marco,
Lovera Marco,
Verhaegen Michel
Publication year - 2013
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2012.0653
Subject(s) - control theory (sociology) , subspace topology , closed loop , identification (biology) , control engineering , computer science , loop (graph theory) , mathematics , artificial intelligence , engineering , control (management) , botany , biology , combinatorics
In this study, the authors present an overview of closed‐loop subspace identification methods found in the recent literature. Since a significant number of algorithms has appeared over the last decade, the authors highlight some of the key algorithms that can be shown to have a common origin in autoregressive modelling. Many of the algorithms found in the literature are variants on the algorithms that are discussed here. In this study, the aim is to give a clear overview of some of the more successful methods presented throughout the last decade. Furthermore, the authors retrace these methods to a common origin and show how they differ. The methods are compared both on the basis of simulation examples and real data. Although the main focus in the literature has been on the identification of discrete‐time models, identification of continuous‐time models is also of practical interest. Hence, the authors also provide an overview of the continuous‐time formulation of the identification framework.