An insight into instrumental variable frequency-domain subspace identification

Akçay, Hüseyin

doi:10.1016/j.automatica.2009.11.009

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…Algorithm 3.1 was proposed in [7] and under mild noise and frequency assumptions [7], [25], it is consistent, that is, Σ c converges to A in its Jordan form w.p.1 as N → ∞.…”

Section: ) Putã Into the Following Jordan Canonical Formmentioning

confidence: 98%

Spectrum estimation in frequency-domain by subspace and regularization-based algorithms: A survey

Akçay

2015

2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation

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In this survey article, we study methods to identify multi-input/multi-output, discrete-time, linear time-invariant systems from power spectrum measurements. First, we examine subspace-based identification algorithms. A hindrance to these methods is splitting of two invariant spaces generated by causal and anti-causal eigenvalues in order to determine model order.Next, we study model order selection criteria based on the regularized nuclear norm and the regularized and reweighted nuclear norm heuristics. The latter heuristic, formulated in a different way, is used to ensure positivity of the spectrum estimate delivered by subspace identification algorithms. A numerical example illustrates properties of the regularized and reweighted nuclear norm heuristic.

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“…Algorithm 3.1 was proposed in [7] and under mild noise and frequency assumptions [7], [25], it is consistent, that is, Σ c converges to A in its Jordan form w.p.1 as N → ∞.…”

Section: ) Putã Into the Following Jordan Canonical Formmentioning

confidence: 98%

Spectrum estimation in frequency-domain by subspace and regularization-based algorithms: A survey

Akçay

2015

2015 IEEE 7th International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation

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“…Subspace-based system identification methods have proven to be efficient for the identification of linear time-invariant systems (LTI), fitting a linear model to input/output or output only measurements taken from a system. An overview of subspace methods can be found in Benveniste and Fuchs (1985); Viberg (1995); Van Overschee and De Moor (1996); Benveniste and Mevel (2007); Akçay (2010). A broad range of applications exists in the identification of processes in automatic control, see e.g.…”

Section: Introductionmentioning

confidence: 99%

Fast multi-order computation of system matrices in subspace-based system identification

Döhler

Mevel

2012

Control Engineering Practice

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Subspace methods have proven to be efficient for the identification of linear time-invariant systems, especially applied to mechanical, civil or aeronautical structures in operation conditions. Therein, system identification results are needed at multiple (over-specified) model orders in order to distinguish the true structural modes from spurious modes using the so-called stabilization diagrams. In this paper, new efficient algorithms are derived for this multi-order system identification with subspace-based identification algorithms and the closely related Eigensystem Realization Algorithm. It is shown that the new algorithms are significantly faster than the conventional algorithms in use. They are demonstrated on the system identification of a large-scale civil structure.

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“…In this paper, the system identification problem of MIMO fractional order systems with timedelay in state is studied. A frequency-domain identification algorithm is presented, which combines genetic algorithm and the traditional subspace method [14][15][16]. The genetic algorithm is utilized to identify fractional differential order and time-delay parameter.…”

Section: Introductionmentioning

confidence: 99%

“…Step 3: Calculate the fitness value according to (14) for each p i , and choose the optimal individuals p o and 0 0 0 0 ( , , , )…”

mentioning

confidence: 99%

A Frequency-Domain Identification Algorithm for MIMO Fractional Order Systems with Time-Delay in State

Zeng

Cheng

Wang

2011

AMR

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The system identification problem of Multi-Input Multi-Output fractional order systems with Time-Delay is studied. A Frequency-Domain identification algorithm is presented, which combines genetic algorithm and subspace method for fractional order systems with time-delay in state. The genetic algorithm is used to identify fractional differential order and Time-Delay parameter. And the state space model is obtained by using frequency-domain subspace method when fractional differential order and time-delay parameter are fixed. Numerical simulation results validate the proposed algorithm.

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An insight into instrumental variable frequency-domain subspace identification

Cited by 5 publications

References 10 publications

Spectrum estimation in frequency-domain by subspace and regularization-based algorithms: A survey

Spectrum estimation in frequency-domain by subspace and regularization-based algorithms: A survey

Fast multi-order computation of system matrices in subspace-based system identification

A Frequency-Domain Identification Algorithm for MIMO Fractional Order Systems with Time-Delay in State

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