The CONTSID toolbox for Matlab: extensions and latest developments

Garnier, Hugues; Gilson, Marion; Laurain, Vincent

doi:10.3182/20090706-3-fr-2004.00122

Cited by 17 publications

(10 citation statements)

References 15 publications

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“…We use a total of N = 1000 input-output data in this example with a sample time period of ∆t = 0.01 seconds to estimate the parameters of (20), i.e. [2,1]. Note that this is performed with zero noise.…”

Section: A Bias Resulting From Initial Conditionsmentioning

confidence: 99%

“…In this contribution, we analyse this bias and develop theoretical support for an upper bound of the continuous-time least squares estimation. We note that this is used as initial step of the so-called RIVC (refined instrumental variable method for continuous system) method [3], [2]. The theoretical result we obtain is then verified by numerical experiments.…”

Section: Introductionmentioning

confidence: 80%

“…For simplicity we consider the noiseless case. The relevant continuous-time method is implemented by using the toolbox Contsid5 [2]. The input-output data for the simulation is calculated in an exact way using MATLAB, since the input signal we consider for the example is a summation of sinewaves.…”

Section: Numerical Evaluationmentioning

confidence: 99%

“…The first method, indirect, is where a discrete time model is first identified and then converted to a continuous-time model [3], [8]. The second method, the direct approach [10], [11], [4], [2] is where a continuous-time model is obtained directly from the sampled data. Unlike the difference equation model in the indirect method, the differential equation model, used in the direct method, is not a linear combination of the sampled input and output signals as it also contains time-derivative terms.…”

Section: Introductionmentioning

confidence: 99%

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Bias analysis of continuous-time model identification from filtered sample output data

Welsh

2012

2012 IEEE 51st IEEE Conference on Decision and Control (CDC)

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The purpose of this paper is to present some issues that arise when applying a traditional continuous-time approach to system identification. In particular, we consider the bias that is introduced into the continuous-time model through the use of sampled data which is filtered to recover the derivative of the continuous time signals. We find that this error has two constituent components, one being related to the sample size, as expected, the other related to the sample period. The results are confirmed by simulation examples.

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Section: A Bias Resulting From Initial Conditionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 80%

Section: Numerical Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Bias analysis of continuous-time model identification from filtered sample output data

Welsh

2012

2012 IEEE 51st IEEE Conference on Decision and Control (CDC)

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“…A detailed investigation of the SRIVC technique is documented in [17]. The implementation of the SRIVC method is available in the CONTSID toolbox for MATLAB [18].…”

Section: A Direct Continuous-time Model Identificationmentioning

confidence: 99%

Nonlinear Dynamic Transformer Time-Domain Identification for Power Converter Applications

O’Driscoll

Ringwood

2014

IEEE Trans. Power Electron.

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Abstract-For flyback converter applications, an accurate model of the transformer is necessary for simulation studies, as well as a basis for model-based controller design. In general, transformer modeling has either focused on the winding model, using frequency-domain methods, or on the nonlinear core model, using time-domain methods. Nonlinear modeling is confined to the time domain and certain difficulties have precluded the use of timedomain methods for winding model estimation, resulting in the lack of integrated modeling approaches. This paper focuses on identifying a complete nonlinear dynamic model of a 3-winding transformer using time-domain system identification approaches. Our study demonstrates a possible way to handle the difficulties of working in the time domain and provides a model at least as accurate as that obtained with the frequency response data. In addition to the parameters of the Jiles-Atherton model, which is used to describe the nonlinear core behavior, the air-gap length is also computed from the experimental data to enhance the core model accuracy. The obtained transformer winding model, core model, and full model are experimentally verified.

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Clarifying cognitive control and the controllable connectome

Medaglia

2018

WIRES Cognitive Science

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Cognitive control researchers aim to describe the processes that support adaptive cognition to achieve specific goals. Control theorists consider how to influence the state of systems to reach certain user-defined goals. In brain networks, some conceptual and lexical similarities between cognitive control and control theory offer appealing avenues for scientific discovery. However, these opportunities also come with the risk of conceptual confusion. Here, I suggest that each field of inquiry continues to produce novel and distinct insights. Then, I describe opportunities for synergistic research at the intersection of these subdisciplines with a critical stance that reduces the risk of conceptual confusion. Through this exercise, we can observe that both cognitive neuroscience and systems engineering have much to contribute to cognitive control research in human brain networks. This article is categorized under: Neuroscience > Cognition Computer Science > Neural Networks Neuroscience > Clinical Neuroscience.

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The CONTSID toolbox for Matlab: extensions and latest developments

Cited by 17 publications

References 15 publications

Bias analysis of continuous-time model identification from filtered sample output data

Bias analysis of continuous-time model identification from filtered sample output data

Nonlinear Dynamic Transformer Time-Domain Identification for Power Converter Applications

Clarifying cognitive control and the controllable connectome

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