Subspace Identification for Linear Systems

Overschee, Peter Van; Moor, Bart De

doi:10.1007/978-1-4613-0465-4

Cited by 2,356 publications

(1,417 citation statements)

References 45 publications

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“…The model can be identified either from correlations (or covariances) of the outputs: Covariance driven stochastic subspace identification-SSI-COV; or directly from time series collected at the tested structure by the use of projections [12]: data driven stochastic subspace identification-SSI-DATA. As reported in [11], these two methods are very closely related.…”

Section: Overview Of Oma Methodsmentioning

confidence: 99%

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Explaining operational modal analysis with data from an arch bridge

Magalhães

Cunha

2011

Mechanical Systems and Signal Processing

247

107

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a b s t r a c tThis tutorial paper aims to introduce the topic of operational modal analysis to nonspecialists on the subject. First of all, it is stressed the relevance of this experimental technique particularly in the assessment of important civil infrastructure. Then, after a synthesis of required theoretical background, three of the most powerful algorithms for output-only modal identification are presented. The several steps of these identification procedures are illustrated with the processing of data collected on a concrete arch bridge with a span of 280 m. As the use of operational modal analysis in the context of structural health monitoring is a subject under active research, this theme is also introduced and briefly exemplified with data continuously recorded at the same bridge.

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Section: Overview Of Oma Methodsmentioning

confidence: 99%

“…These are described and proven in [12]. The most important property is the following relation between the correlation matrix of the measured structural responses and the state-space matrix:…”

Section: Stochastic State-space Modelmentioning

confidence: 99%

Explaining operational modal analysis with data from an arch bridge

Magalhães

Cunha

2011

Mechanical Systems and Signal Processing

247

107

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“…The output is the roll angle ϕ . Numerous system identification techniques were applied to the data through the use of MATLAB System Identification Toolbox, but the best results were achieved with subspace identification (N4SID) [12]. The discretized version of this model has the transfer function where the sampling period is = 0.025 s, which is the rate that we process data for our particular hardware/software configuration.…”

Section: System Identificationmentioning

confidence: 99%

Design of a Quadrotor Roll Controller Using System Identification to Improve Empirical Results

Batmaz¹,

Elbir²,

Kasnakoğlu³

2013

IJMMM

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Abstract-Unmanned Aerial Vehicles (UAVs), specifically quadrotors, is one of the major topics of study in current literature with applications to numerous fields. In this paper we consider a test-bed for the design of a low cost flight controller for a quadrotor and as a first step in design we demonstrate the design of the roll controller on an experimental setup through the stages of data collection, modeling, control design and verification. The procedure consists of four stages: 1) Experimental determination of controller coefficients, 2) Data collection, 3) System identification, 4) Controller redesign by tuning coefficients with a numerical search. It is observed that the system designed as such is capable of achieving the desired roll stabilization, and coefficient tuning on the identified model noticeably improves the settling time and steady state oscillation amplitude.

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“…Along this line, it is of interest to extend the nonparametric paradigm to the design of optimal predictors. By the way, predictor estimation, beyond being of interest on its own, is the preliminary step of subspace identification methods [9], [10], [11], [12]. Therefore, improving predictor design may enhance performance of subspace identification methods as well.…”

Section: Introductionmentioning

confidence: 99%

Predictor estimation via Gaussian regression

Pillonetto

Chiuso²,

Nicolao

2008

2008 47th IEEE Conference on Decision and Control

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Abstract-A novel nonparametric paradigm to model identification has been recently proposed where, in place of postulating finite-dimensional models of the system transfer function, the system impulse response is searched for within an infinitedimensional space. In this paper, we extend such nonparametric approach to the design of optimal predictors by interpreting the predictor coefficients as realizations of Gaussian processes. Numerical experiments, where data are generated by ARMAX models, are used to show advantages of the new approach in terms of both predictive capability on new data and accuracy in reconstruction of predictor coefficients. In a companion paper, it is also shown how this new approach to predictor design may greatly enhance performance of subspace identification methods.

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Subspace Identification for Linear Systems

Cited by 2,356 publications

References 45 publications

Explaining operational modal analysis with data from an arch bridge

Explaining operational modal analysis with data from an arch bridge

Design of a Quadrotor Roll Controller Using System Identification to Improve Empirical Results

Predictor estimation via Gaussian regression

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