Multiple model LPV approach to nonlinear process identification with EM algorithm

Jin, Xing; Huang, Biao; Shook, David S.

doi:10.1016/j.jprocont.2010.11.008

Cited by 102 publications

(66 citation statements)

References 15 publications

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“…Perhaps, the most common type of empirical model is a linear model. When a process system exhibits significant nonlinearities as is the case in most chemical processes, the use of multiple linear models has been employed to improve the accuracy of prediction over a larger operating region [38,39,36]. One potential grouping of the various methods of system identification is to group the methods on the basis of the type of empirical model derived which may be either an input-output model or a state-space model.…”

Section: Introductionmentioning

confidence: 99%

Economic model predictive control of nonlinear process systems using empirical models

2014

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Economic model predictive control (EMPC) is a feedback control technique that attemptsto tightly integrate economic optimization and feedback control since it is a predictive control scheme that is formulated with an objective function representing the process economics. As its name implies, EMPC requires the availability of a dynamic model to compute its control actions and such a model may be obtained either through application of first-principles or though system identification techniques. However, in industrial practice, it may be difficult in general to obtain an accurate first-principles model of the process. ii The thesis of Anas Wael Alanqar is approved.

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Section: Introductionmentioning

confidence: 99%

Economic model predictive control of nonlinear process systems using empirical models

2014

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“…Estimation of linear parameter-varying (LPV) polynomial models in an input-output (IO) setting has received a significant attention recently in the identification literature (see, e.g., [1]- [7]). In discrete-time, the most basic model structure in this context is the so-called auto-regressive model with exogenous input (ARX) which is often defined in the singleinput single-output (SISO) case as…”

Section: Introductionmentioning

confidence: 99%

Model structure learning: A support vector machine approach for LPV linear-regression models

Tóth

Laurain

Zheng

et al. 2011

IEEE Conference on Decision and Control and European Control Conference

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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%

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

110

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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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Multiple model LPV approach to nonlinear process identification with EM algorithm

Cited by 102 publications

References 15 publications

Economic model predictive control of nonlinear process systems using empirical models

Economic model predictive control of nonlinear process systems using empirical models

Model structure learning: A support vector machine approach for LPV linear-regression models

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

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