Continuous-Time Linear Parameter-Varying Identification of a Cross Flow Heat Exchanger: A Local Approach

Mercère, Guillaume; Pálsson, Halldór; Poinot, Thierry

doi:10.1109/tcst.2010.2071874

Cited by 44 publications

(12 citation statements)

References 37 publications

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“…Some successful methods for LPV system identification have been reported recently [17,8,5,13,21,10]. In the local approach to LPV system identification, interpolation is essential to establishing global models from a collection of locally estimated linear time invariant (LTI) models [14,2,1].…”

Section: Introductionmentioning

confidence: 99%

From structurally independent local LTI models to LPV model

Zhang

Ljung

2017

Automatica

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The local approach to linear parameter varying (LPV) system identification consists in interpolating individually estimated local linear time invariant (LTI) models corresponding to fixed values of the scheduling variable. It is shown in this paper that, without any global structural assumption of the considered LPV system, individually estimated local state-space LTI models do not contain su cient information for determining similarity transformations making them coherent. It is possible to estimate these similarity transformations from input-output data under appropriate excitation conditions.

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Section: Introductionmentioning

confidence: 99%

From structurally independent local LTI models to LPV model

Zhang

Ljung

2017

Automatica

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“…The study demonstrated in addition, the benefit of including models into the design phase and introduced a modified linear model that aided the determination of the optimum design parameters. Various other methods exist which include the neural networks (Riverol and Napolitano, 2005), wavelets (Ingimundardóttir and Lalot, 2011), linear parameter varying (LPV) models (Mercère et al, 2013), fuzzy observers (Delmotte et al, 2008), physical model (Gudmundsson et al, 2009), and Extended Kalman filters (Jonsson et al, 2007). Moreover, Wen et al (2017) employed a multi-resolution wavelet neural network approach for the prediction of fouling resistance of a plate heat exchanger.…”

Section: Models For Predicting Foulingmentioning

confidence: 99%

Fouling Phenomenon and Its Effect on Heat Exchanger: A Review

Ajayi¹,

Ogbonnaya²

2017

Frontiers in Heat and Mass Transfer

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Heat exchangers as a heat transfer device has gained wide applications across different levels of domestic and industrial set-ups. Various studies have been carried out to study, analyse and predict its performance. However, one major phenomenon that limits heat exchanger performance is attributed to fouling. Based on this, various studies and approaches have focused on reduction, elimination and mitigation of fouling. This study therefore focused on this. It reviewed several attempts that have been carried out to understand and mitigate the incidents of fouling in heat exchangers. The study found that despite the existing models developed towards understanding fouling, there is no single model that has provided an accurate prediction of fouling in all the different types of heat exchangers. Also, majority of existing results have limited applications and some of the solutions are only peculiar to particular type of heat exchanger. Further to this, majority of the study results only pointed to small scale laboratory test schemes and there is absence of in-situ measurements that predicts actual (real life) service performance.

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“…Moreover, the LPV models allow the extension of linear design techniques to nonlinear systems [1]. Because of these advantages, there are many researches in LPV systems [2][3][4]. A number of LPV model applications have emerged, including wind turbines [5], biomedical applications [6], and leakage detection [7].…”

Section: Introductionmentioning

confidence: 99%

Data-driven predictive control for continuous-time linear parameter varying systems with application to wind turbine

Luo

2017

Int. J. Control Autom. Syst.

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A new data-driven predictive control method based on subspace identification for continuous-time linear parameter varying (LPV) systems is presented in this paper. It is developed by reformulating the continuous-time LPV system which utilizes Laguerre filters to obtain the subspace prediction of output. The subspace predictors are derived by QR decomposition of input-output and Laguerre matrices obtained by input-output data. The predictors are then applied to design the model predictive controller. It is shown that the integrated action is incorporated in the control effect to eliminate the steady-state offset. We control the continuous-time LPV systems to obtain the attractive performance with the proposed data-driven predictive control method. The proposed controller is applied to a wind turbine to verify its effectiveness and feasibility.

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Continuous-Time Linear Parameter-Varying Identification of a Cross Flow Heat Exchanger: A Local Approach

Cited by 44 publications

References 37 publications

From structurally independent local LTI models to LPV model

From structurally independent local LTI models to LPV model

Fouling Phenomenon and Its Effect on Heat Exchanger: A Review

Data-driven predictive control for continuous-time linear parameter varying systems with application to wind turbine

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