Subspace identification from closed loop data

Ljung, Lennart; McKelvey, Tomas

doi:10.1016/0165-1684(96)00054-0

Cited by 152 publications

(86 citation statements)

References 8 publications

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“…More precisely, the use of past input and/or past output as instrumental variable (IV) gives unreliable estimates because of the correlation between the disturbances and the input signal (see (Ljung and McKelvey, 1996) for the proof). To overcome this drawback, a particular IV based on the extended instrumental variable techniques (Ljung, 1999) is proposed.…”

Section: Optimal Closed-loop Instrumental Variable Methodsmentioning

confidence: 99%

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Subspace-Based Optimal Iv Method for Closed-Loop System Identification

Gilson

Mercère

2006

IFAC Proceedings Volumes

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This paper deals with an optimal instrumental variable method dedicated to subspace-based closed-loop system identification. The presented solution is based on the MOESP technique but requires to modify the original scheme by proposing a new PO MOESP method which uses reconstructed past input and past output data as instrumental variables. The developed approach is then illustrated via a simulation example and a comparison with other subspace-based methods.

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Section: Optimal Closed-loop Instrumental Variable Methodsmentioning

confidence: 99%

“…• the closed-loop subspace method proposed by Ljung and McKelvey (1996), referenced here as sscl, • the closed-loop 4sid solution proposed by Van Overschee and De Moor (1997), referenced here as 4sidcl.…”

Section: Simulation Examplementioning

confidence: 99%

Subspace-Based Optimal Iv Method for Closed-Loop System Identification

Gilson

Mercère

2006

IFAC Proceedings Volumes

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“…The covariance matrix of the innovation process is given by Ξ := C s PC T s +R, so that we see from (11) and (16) …”

Section: Identification Of Stochastic Subsystemmentioning

confidence: 99%

A Realization-Based Method for Identification of Closed-Loop Systems Using Measurable Disturbance

Ase¹,

Katayama

2009

SICE Journal of Control, Measurement, and System Integration

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: We derive a closed-loop subspace identification method based on a realization theory without using probing inputs, but with the assumption that there is a measurable disturbance that can be used as a test input for closed-loop identification. The orthogonal decomposition (ORT) technique is applied to obtain deterministic and stochastic components of the joint input-output process. An ORT-based closed-loop subspace identification method is then developed by combining realizations of the deterministic and stochastic components, followed by a model reduction procedure to obtain lower dimensional models. Some numerical results are included to show the applicability of the present method.

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“…Last implementation issue is concerned with the leastsquares problem (19). Similarly to (9) we perform an LQdecomposition…”

Section: B Numerical Implementation: Computing Projectionsmentioning

confidence: 99%

Multi-step-ahead multivariate predictors: A comparative analysis

Cescon

Johansson

2010

49th IEEE Conference on Decision and Control (CDC)

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Abstract-The focus of this article is to undertake a comparative analysis of multi-step-ahead linear multivariate predictors. The approach considered for the estimation will be based on geometrically reliable linear algebra tools, resorting to subspace identification methods. A crucial issue is quantification of both bias error and variance affecting the estimate of the prediction for increasing values of the look ahead when only a small number of samples is available. No complete theory is available so far, nor sufficient numerical experience. Therefore, the analysis of this paper aims at shading some lights on the topic providing some insights and help to develop some intuitions.

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Subspace identification from closed loop data

Cited by 152 publications

References 8 publications

Subspace-Based Optimal Iv Method for Closed-Loop System Identification

Subspace-Based Optimal Iv Method for Closed-Loop System Identification

A Realization-Based Method for Identification of Closed-Loop Systems Using Measurable Disturbance

Multi-step-ahead multivariate predictors: A comparative analysis

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