Closed loop subspace system identification

We present a general framework for closed loop subspace system identification. This framework consists of two new projection theorems which allow the extraction of non-steady state Kalman filter states and of system related matrices directly from input output data. Three algorithms for the identific...

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Bibliographic Details
Published inProceedings of the 36th IEEE Conference on Decision and Control Vol. 2; pp. 1848 - 1853 vol.2
Main Authors Van Overschee, P., De Moor, B.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1997
Subjects
Data mining
Error correction
Feedback
Instruments
Intelligent systems
MIMO
Optimal control
Optimization methods
State-space methods
System identification
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Summary:We present a general framework for closed loop subspace system identification. This framework consists of two new projection theorems which allow the extraction of non-steady state Kalman filter states and of system related matrices directly from input output data. Three algorithms for the identification of the state space matrices can be derived from these theorems. The similarities between the theorems and algorithms, and the corresponding open loop theorems and algorithms in the literature are remarked on.
ISBN:0780341872
9780780341876
ISSN:0191-2216
DOI:10.1109/CDC.1997.657851