Multimodel identification for control of an ill-conditioned distillation column

Häggblom, Kurt E.; Böling, Jari M.

doi:10.1016/s0959-1524(97)00040-1

Cited by 34 publications

(24 citation statements)

References 13 publications

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“…It has been found that it is easier to obtain good information about the high-gain direction than the low-gain direction (Koung and MacGregor, 1993). Explicit excitation of the low-gain direction is required to obtain a model including the low-gain properties; otherwise, the model may be inadequate for control design (Koung and MacGregor, 1993;Häggblom and Böling, 1998).…”

Section: Introductionmentioning

confidence: 99%

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Control-relevant input excitation for system identification of ill-conditioned n × n systems with n > 2

Ghosh

Häggblom

Böling

2014

IFAC Proceedings Volumes

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Section: Introductionmentioning

confidence: 99%

“…A considerable amount of literature exists on identification of ill-conditioned systems (e.g., Koung and MacGregor, 1993;Häggblom and Böling, 1998, 2013, Lee at al, 2003Zhu and Stec, 2006;Rivera et al, 2009). However, most studies are limited to 22  systems.…”

Section: Introductionmentioning

confidence: 99%

Control-relevant input excitation for system identification of ill-conditioned n × n systems with n > 2

Ghosh

Häggblom

Böling

2014

IFAC Proceedings Volumes

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“…Zhu [4] in (1999) used Wiener model to identify a continuous distillation column. Haggblom and Boling [5] worked on multimodel identification for control of an ill-conditioned distillation column.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Identification of a Pilot Distillation Column for Control and Estimation using Genetic Algorithms

Meshksar

Khayatian

Jenabali

et al. 2007

IECON 2007 - 33rd Annual Conference of the IEEE Industrial Electronics Society

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A system identification problem can be considered as an optimization task where the purpose is to find a model and a set of parameters that minimize the prediction error between the plant outputs and the model outputs. In some cases, the objective function is not mathematically and analytically available, like in distillation column, in addition the obtained parameters may not be globally optimal. In these situations, Genetic Algorithms are superior to gradient descent parameter learning algorithms. The idea of this paper is to identify unknown parameters of the distillation column model. The novelty and contribution of this article is in applying the genetic algorithm in identification of distillation column. Two different models are investigated and their step responses are compared to each other.

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“…[1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18] Despite the recent advances of LMN, prior knowledge of the process, which may not be readily accessible in most practical applications, has to be exploited for the determination of the LMN structure. To circumvent this problem, Ge et al 7 developed an extended self-organizing map (ESOM) network to partition the operating space of the nonlinear process automatically using the plant data.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Control of a Nonlinear Process Based on the Extended Self-Organizing Map Network

Zhuang

Ang

Ohshima

et al. 2002

Ind. Eng. Chem. Res.

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An extended self-organizing map (ESOM) network, which consists of a self-organization phase and an optimization phase, was recently developed to construct a local model network (LMN) automatically using the plant data. However, this previous result suffers two drawbacks: (1) increased computation time in the self-organization phase as the number of local models increases, (2) lack of checking stability conditions for both local models and LMN. To overcome these problems, an improved algorithm for the ESOM network is developed in this paper by employing a competitive learning algorithm for data clustering in the self-organization phase and parametric constraints are formulated in the optimization phase to handle the stability of local models. In addition, the global stability of LMN is addressed. With LMN constructed by the ESOM network, it serves as a basis for building a nonlinear controller that combines several local controllers through the weighting functions obtained by the ESOM algorithm. Literature examples are used to illustrate the proposed ESOM-based modeling and controller design method.

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Multimodel identification for control of an ill-conditioned distillation column

Cited by 34 publications

References 13 publications

Control-relevant input excitation for system identification of ill-conditioned n × n systems with n > 2

Control-relevant input excitation for system identification of ill-conditioned n × n systems with n > 2

Modeling and Identification of a Pilot Distillation Column for Control and Estimation using Genetic Algorithms

Modeling and Control of a Nonlinear Process Based on the Extended Self-Organizing Map Network

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