Identification of multi-model LPV models with two scheduling variables

Huang, Jiangyin; Ji, Guoli; Zhu, Yucai; Bosch, Paul

doi:10.1016/j.jprocont.2012.05.006

Cited by 47 publications

(32 citation statements)

References 19 publications

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“…In the LPV modeling framework, where the interpolated points can correspond to model outputs or locally identified samples of the coefficient functions a i and b j (see Section 3.4), the following "standard" approaches are commonly used [10,19,27,36].…”

Section: Interpolation Methodsmentioning

confidence: 99%

“…Selection of the model structure and the interpolation scheme as well as the number and location of the used operating points are application specific (e.g., see [10]). Some of the local approaches have been recently applied on distillation columns [35,27], on fermentation processes [36] and on a continuous tank reactor [28,36]. In the sequel, we intend to give an overview on all available methods for the LPV modeling of process systems in the local setting.…”

Section: Lpv Identification Using the Local Approachmentioning

confidence: 99%

“…The first perspective, called the local approach, relies on the identification of multiple LTI models at several operating points of the process, which is followed by the interpolation of the resulting models over the entire operating range. Several widely used interpolation schemes and methods (see [19,[26][27][28][29][30]21,17]) are studied. The other investigated LPV identification approach, called the global approach, is based on a single data set that spans over a large range of operating conditions.…”

Section: Introductionmentioning

confidence: 99%

“…The primary motivation for investigating the above mentioned local and global approaches for data-driven modeling of a highpurity distillation column originates from [35,27], where significant difficulties are reported for the local LPV identification approaches. The objective of this work is twofold: (i) provide a complete overview of the local LPV approaches given in [27] and (ii) investigate the open questions related to the performance of local and global LPV approaches.…”

Section: Introductionmentioning

confidence: 99%

“…The objective of this work is twofold: (i) provide a complete overview of the local LPV approaches given in [27] and (ii) investigate the open questions related to the performance of local and global LPV approaches. It is observed that directionality and changing effective order of local models lead to degradation of the local estimates of the process dynamics using IO model structures, whereas series expansion model structures based approaches (e.g., the OBF method) and global estimation techniques tend to describe the process dynamics adequately.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A review on data-driven linear parameter-varying modeling approaches: A high-purity distillation column case study

Bachnas

Tóth

Ludlage

et al. 2014

Journal of Process Control

108

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a b s t r a c tModel-based control strategies are widely used for optimal operation of chemical processes to respond to the increasing performance demands in the chemical industry. Yet, obtaining accurate models to describe the inherently nonlinear, time-varying dynamics of chemical processes remains a challenge in most model-based control applications. This paper reviews data-driven, Linear Parameter-Varying (LPV) modeling approaches for process systems by exploring and comparing various identification methods on a high-purity distillation column case study. Several LPV identification methods that utilize input-output and series expansion model structures are explored. Two LPV identification perspectives are adopted: (i) the local approach, which corresponds to the interpolation of Linear Time-Invariant (LTI) models identified at different steady-state operating points of the system and (ii) the global approach, where a parametrized LPV model structure is identified directly using a global data set with varying operating points. For the local approach, various model interpolation schemes are studied under an Output Error (OE) noise setting, whereas in the global case, a polynomial parametrization based OE prediction error minimization approach, an Orthonormal Basis Functions (OBFs) based model estimator and a LeastSquare Support Vector Machine (LS-SVM) based non-parametric approach are investigated. Through extensive simulation studies, the aforementioned LPV identification approaches are analyzed in terms of the attainable model accuracy and local frequency response behavior of the obtained models. Recommendations are provided to achieve adequate choice between the methods for a particular process system at hand.

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Section: Interpolation Methodsmentioning

confidence: 99%

Section: Lpv Identification Using the Local Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A review on data-driven linear parameter-varying modeling approaches: A high-purity distillation column case study

Bachnas

Tóth

Ludlage

et al. 2014

Journal of Process Control

108

View full text Add to dashboard Cite

show abstract

Identification of nonlinear time‐varying systems using wavelet neural networks

Emami

Roudbari

2020

Adv Control Appl

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The dynamic model of an aircraft changes significantly by altering the flight speed and the vehicle altitude. Thus, a conventional aircraft has a nonlinear time‐varying dynamic model in different regions of the flight envelope, and a dynamic model developed for a specific operating point is not valid in the entire flight envelope. This paper presents a novel identification approach that can deal with nonlinear and time‐varying characteristics of complex dynamic systems, especially an aerial vehicle in the entire flight envelope. In this regard, a set of local submodels are first developed at different operating points of the system, and subsequently, a multimodel structure is introduced to aggregate the outputs of the local models as a single model. Wavelet Neural Networks (WNNs), which combine both the universal approximation property of neural networks and the wavelet decomposition capability, are used as the local models of the proposed scheme. Also, three different approaches for determining the validity functions of the local models are introduced to allow for identifying the time‐varying dynamics of the system. The simulation results obtained for the Generic Transport Model aircraft suggest that the proposed WNN‐based multimodel structure can be used satisfactorily as the prediction model of model‐based flight control systems for long prediction horizons.

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Nonlinear process identification in the presence of multiple correlated hidden scheduling variables with missing data

et al. 2015

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Identification of nonlinear processes in the presence of noise corrupted and correlated multiple scheduling variables with missing data is concerned. The dynamics of the hidden scheduling variables are represented by a state‐space model with unknown parameters. To assure generality, it is assumed that the multiple correlated scheduling variables are corrupted with unknown disturbances and the identification dataset is incomplete with missing data. A multiple model approach is proposed to formulate the identification problem of nonlinear systems under the framework of the expectation‐maximization algorithm. The parameters of the local process models and scheduling variable models as well as the hyperparameters of the weighting function are simultaneously estimated. The particle smoothing technique is adopted to handle the computation of expectation functions. The efficiency of the proposed method is demonstrated through several simulated examples. Through an experimental study on a pilot‐scale multitank system, the practical advantages are further illustrated. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3270–3287, 2015

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Identification of multi-model LPV models with two scheduling variables

Cited by 47 publications

References 19 publications

A review on data-driven linear parameter-varying modeling approaches: A high-purity distillation column case study

A review on data-driven linear parameter-varying modeling approaches: A high-purity distillation column case study

Identification of nonlinear time‐varying systems using wavelet neural networks

Nonlinear process identification in the presence of multiple correlated hidden scheduling variables with missing data

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