Improved frequency response model identification method for processes with initial cyclic‐steady‐state

Cheon, Yu Jin; Sung, Su Whan; Lee, Jietae; Je, Cheol Ho; Lee, In‐Beum

doi:10.1002/aic.12550

Cited by 13 publications

(5 citation statements)

References 9 publications

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“…The usage of such tools generally produces more accurate results at the expense of requiring more time to be obtained. Some of these methods are based on the Fourier transform [4,5,37], another work defines the Alocus to identify low order systems [10], and others propose to take into account the effect of harmonics in the response [14]. Other authors also consider the shape of the induced oscillation for the identification procedure [21,36].…”

Section: Some Work Have Been Conducted Aiming To Extract Morementioning

confidence: 99%

Multiple frequency response points identification through single asymmetric relay feedback experiment

Miguel-Escrig

Romero-Pérez²,

Moreno³

et al. 2023

Automatica

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Section: Some Work Have Been Conducted Aiming To Extract Morementioning

confidence: 99%

Multiple frequency response points identification through single asymmetric relay feedback experiment

Miguel-Escrig

Romero-Pérez²,

Moreno³

et al. 2023

Automatica

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“…The accuracy of these models, therefore, becomes a cornerstone for the successful application of model-based control strategies, catalyzing the development and deployment of various process identification methods. Consequently, various process identification methodologies have been developed and implemented across diverse industrial domains [9][10][11][12][13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

A Robust Process Identification Method under Deterministic Disturbance

Yook,

Chu,

et al. 2024

Processes

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This study introduces a novel process identification method aimed at overcoming the challenge of accurately estimating process models when faced with deterministic disturbances, a common limitation in conventional identification methods. The proposed method tackles the difficult modeling problems due to deterministic disturbances by representing the disturbances as a linear combination of Laguerre polynomials and applies an integral transform with frequency weighting to estimate the process model in a numerically robust and stable manner. By utilizing a least squares approach for parameter estimation, it sidesteps the complexities inherent in iterative optimization processes, thereby ensuring heightened accuracy and robustness from a numerical analysis perspective. Comprehensive simulation results across various process types demonstrate the superior capability of the proposed method in accurately estimating the model parameters, even in the presence of significant deterministic disturbances. Moreover, it shows promising results in providing a reasonably accurate disturbance model despite structural disparities between the actual disturbance and the model. By improving the precision of process models under deterministic disturbances, the proposed method paves the way for developing refined and reliable control strategies, aligning with the evolving demands of modern industries and laying solid groundwork for future research aimed at broadening application across diverse industrial practices.

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“…However, as the describing function (DF) based methods give points in the Nyquist map whose accuracy depends on the filtering capacity of the process, other methods have been proposed based on different modifications of the Fourier transform (Cheon et al, 2010;Cheon et al, 2011;Ma & Zhu, 2006;Sung & Lee, 2000;Wang et al, 1997;. All these algorithms have in common that they use all the process data from the initial transient region to the final cyclic steady-state part of the experiment.…”

Section: Introductionmentioning

confidence: 99%

Asymmetric delayed relay feedback identification based on the n -shifting approach

Moreno

Dormido

Miguel-Escrig

et al. 2021

International Journal of Control

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The paper presents an improvement of the n-shifting technique to identify the frequency response of an industrial process using a fully asymmetric and delaying relay. The n-shifting approach allows the calculation of n + 1 points of G(s) by an asymmetric relay experiment. This set of n points is composed of G(0), G(jω osc ), ... , G(jnω osc ), being ω osc the oscillation frequency, and where G(jω osc ) is in most cases located in the third quadrant of the Nyquist map. By delaying the relay output and repeating a similar experiment it can be generated n additional points of G(s) where the first point is G(jω' osc ) with 0 < ω' osc < ω osc . In this way, it is possible to depict the full output spectrum of G(s) from zero to very high frequencies by a short relay experiment. An example of identification and tuning of a PID controller with data from the n-shifting are presented to show the validity of the approach.

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Improved frequency response model identification method for processes with initial cyclic‐steady‐state

Cited by 13 publications

References 9 publications

Multiple frequency response points identification through single asymmetric relay feedback experiment

Multiple frequency response points identification through single asymmetric relay feedback experiment

A Robust Process Identification Method under Deterministic Disturbance

Asymmetric delayed relay feedback identification based on the n -shifting approach

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