Journal Article10.1016/0165-1684(96)00054-0
Subspace identification from closed loop data
Lennart Ljung,Tomas McKelvey +1 more
194
TL;DR: The present paper stresses how the basic idea is to focus on the estimation of the state-variable candidates — the k-step ahead output predictors — by recomputing these from a ‘non-parametric’ (or, rather, high order ARX) one- step ahead predictor model, closed loop data can be handled.
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About: This article is published in Signal Processing. The article was published on 02 Jul 1996. The article focuses on the topics: Subspace topology & Linear subspace.
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Citations
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References
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System Identification: Theory for the User
Lennart Ljung
- 01 Jan 1987
TL;DR: Das Buch behandelt die Systemidentifizierung in dem theoretischen Bereich, der direkte Auswirkungen auf Verstaendnis and praktische Anwendung der verschiedenen Verfahren zur IdentifIZierung hat.
N4SID: subspace algorithms for the identification of combined deterministic-stochastic systems
TL;DR: Two new N4SID algorithms to identify mixed deterministic-stochastic systems are derived and these new algorithms are compared with existing subspace algorithms in theory and in practice.
2.1K
Identification of the deterministic part of MIMO state space models given in innovations form from input-output data
TL;DR: Two algorithms to identify a linear, time-invariant, finite dimensional state space model from input-output data and a special case of the recently developed Multivariable Output-Error State Space (MOESP) class of algorithms based on instrumental variables are described.
976
Subspace algorithms for the stochastic identification problem
Peter Van Overschee,Bart De Moor +1 more
TL;DR: A new subspace algorithm is derived to consistently identify stochastic state space models from given output data without forming the covariance matrix and using only semi-infinite block Hankel matrices.
520
Subspace algorithms for the stochastic identification problem
P. Van Overschee,B. De Moor +1 more
- 11 Dec 1991
TL;DR: In this article, the authors derive a novel algorithm to consistently identify stochastic state space models from given output data without forming the covariance matrix and using only semi-infinite block Hankel matrices.