Alternative model structure with simplistic noise model to identify linear time invariant systems subjected to non-stationary disturbances

Karra, Srinivas; Karim, M. Nazmul

doi:10.1016/j.jprocont.2008.12.007

Cited by 19 publications

(28 citation statements)

References 25 publications

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“…When performing an identification test for a sampled system with time delay subject to load disturbance and measurement noise, the output response can be generally described by the following discrete-time OE model with an integer type delay parameter, The identification objective is to estimate the above OE model parameters including an integer delay from sampled data, with a prior knowledge or assumption on the orders of a n and b n for model fitting. For unknown system dynamics, the optimal model order may be determined by using the Akaike information criterion (AIC), a hypothesis testing condition [14], or a cross-correlation function between the input and the univariate residual sequence [19], so as to check if a higher order model could result in better fitting in terms of the parsimony principle on the number of model parameters.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…A refined instrumental variable (RIV) approach was recently developed by using a unified operator to estimate the Box-Jenkins model [18]. For the presence of non-stationary disturbance, a bias compensation identification algorithm [19] was given to obtain an extended ARMAX or OE model with good accuracy, by using a variable forgetting factor to estimate the model parameters and disturbance. For time delay systems, only a few papers presented discrete-time domain identification methods for obtaining an OE model with an integer type delay parameter, due to the difficulties for identifying the linear model parameters together with an integer type delay parameter that involves mixed-integer programming, which was recognized to be a non-convex problem for parameter estimation [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

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Identification of discrete-time output error model for industrial processes with time delay subject to load disturbance

Dong

Liu

Wang

et al. 2017

Journal of Process Control

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In this paper, a bias-eliminated output error model identification method is proposed for industrial processes with time delay subject to unknown load disturbance with deterministic dynamics. By viewing the output response arising from such load disturbance as a dynamic parameter for estimation, a recursive least-squares identification algorithm is developed in the discrete-time domain to estimate the linear model parameters together with the load disturbance response, while the integer delay parameter is derived by using a one-dimensional searching approach to minimize the output fitting error. An auxiliary model is constructed to realize consistent estimation of the model parameters against stochastic noise. Moreover, dual adaptive forgetting factors are introduced with tuning guidelines to improve the convergence rates of estimating the model parameters and the load disturbance response, respectively. The convergence of model parameter estimation is analyzed with a rigorous proof. Illustrative examples for open-and closed-loop identification are shown to demonstrate the effectiveness and merit of the proposed identification method.

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Section: Problem Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification of discrete-time output error model for industrial processes with time delay subject to load disturbance

Dong

Liu

Wang

et al. 2017

Journal of Process Control

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“…Whereas, a sluggish tuning produces a slowly-varying input, which is less exciting for the process, and possibly less informative for any identification procedure. The impact of controller tuning has already been studied by [27], for the identification of a pure linear dynamics without considering the problem of valve stiction. In addition, the same authors ( [17], Chp.…”

Section: Effect Of Controller Tuningmentioning

confidence: 99%

“…This approach is robust, but obviously heavy in terms of computational load. Among other standard solutions to estimate the time delay, [22] and [27] have proposed a cross correlation analysis between the input (MV) and the output (PV) sequence. Additional simulations with unknown process time delay have showed that t d has no significant impact on the identification methods.…”

Section: Effect Of Controller Tuningmentioning

confidence: 99%

“…Two well-established stic-103 tion models are used to describe the nonlinear valve dynamics: 104 Kano's [7] and He's [8] model. Five different models describe 105 the linear process dynamics: ARX (Auto Regressive model 106 with eXternal input), ARMAX (Auto Regressive Moving Aver-107 age with eXternal input), SS (State Space model), EARX (Ex-108 tended Auto Regressive model with eXternal input), EARMAX 109 (Extended Auto Regressive Moving Average with eXternal in-110 put,[27]). …”

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confidence: 99%

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System identification applied to stiction quantification in industrial control loops: A comparative study

Capaci

Scali

Pannocchia

2016

Journal of Process Control

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A comparative study of different models and identification techniques applied to the quantification of valve stiction in industrial control loops is presented in this paper, with the objective of taking into account for the presence of external disturbances. A Hammerstein system is used to model the controlled process (linear block) and the sticky valve (nonlinear block): five different candidates for the linear block and two different candidates for the nonlinear block are evaluated and compared. Two of the five linear models include a nonstationary disturbance term that is estimated along with the input-to-output model, and these extended models are meant to cope with situations in which significant nonzero mean disturbances affect the collected data. The comparison of the different models and identification methods is carried out thoroughly in three steps: simulation, application to pilot plant data and application to industrial loops. In the first two cases (simulation and pilot plant) the specific source of fault (stiction with/without external disturbances) is known and hence a validation of each candidate can be carried out more easily. Nonetheless, each fault case considered in the previous two steps has been found in the application to a large number of datasets collected from industrial loops, and hence the merits and limitations of each candidate have been confirmed. As a result of this study, extended models are proved to be effective when large, time varying disturbances affect the system, whereas conventional (stationary) noise models are more effective elsewhere

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Adaptive control and signal processing literature survey (No. 14)

2009

Adaptive Control & Signal

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In order to keep readers up to date, the journal will contain a list of recently published journal articles relevant to the fields of adaptive control and signal processing six times a year. This list is compiled from a wide range of journals. Please note that even though the list has been broadly categorized these classifications are by no means strict, and are there to assist the reader. Please also note that inclusion in the list is not an endorsement of a paper's quality. If you have any suggestions or comments, please gain of periodic linear switched systems: Fast switching behavior. IEEE Transactions on Automatic Control 2009; 54(7):1632-1637. Santarelli K, Dahleh M. L 2 gain stability of switched output feedback controllers for a class of LTI systems. IEEE Transactions on Automatic Control 2009; 54(7):1504-1514. Sun Z. Stabilizing switching design for switched linear systems: a state-feedback path-wise switching approach. Automatica 2009; 45(7):1708-1714. Wang J, Qu Z. Robust adaptive control of a class of nonlinearly parameterised time-varying uncertain systems. IET Control Theory and Applications 2009; 3(6):617-630. Xie D, Wang Q, Wu Y. Average dwell-time approach to L2 gain control synthesis of switched linear systems with time delay in detection of switching signal.

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Alternative model structure with simplistic noise model to identify linear time invariant systems subjected to non-stationary disturbances

Cited by 19 publications

References 25 publications

Identification of discrete-time output error model for industrial processes with time delay subject to load disturbance

Identification of discrete-time output error model for industrial processes with time delay subject to load disturbance

System identification applied to stiction quantification in industrial control loops: A comparative study

Adaptive control and signal processing literature survey (No. 14)

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