Continuous-Discrete Time Prediction-Error Identification Relevant for Linear Model Predictive Control

John Bagterp Jørgensen; Sten Bay Jørgensen

Continuous-Discrete Time Prediction-Error Identification Relevant for Linear Model Predictive Control

John Bagterp Jørgensen, Sten Bay Jørgensen

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Abstract

A Prediction-error-method tailored for model based predictive control is presented. The prediction-error method studied are based on predictions using the Kalman filter and Kalman predictors for a linear discrete-time stochastic state space model. The linear discrete-time stochastic state space model is realized from a continuous-discrete-time linear stochastic system specified using transfer functions with time-delays. It is argued that the prediction-error criterion should be selected such that it is compatible with the objective function of the predictive controller in which the model is to be applied. The suitability of the proposed prediction error-method for predictive control is demonstrated for dual composition control of a simulated binary distillation column.

Original language	English
Title of host publication	Proceedings of the European Control Conference (ECC '07 )
Publisher	IEEE
Publication date	2007
Pages	4752-4758
ISBN (Electronic)	978-3-9524173-8-6
Publication status	Published - 2007
Event	European Control Conference 2007 - Kos, Greece Duration: 2 Jul 2007 → 5 Jul 2007 http://ecc07.ntua.gr/

Conference

Conference	European Control Conference 2007
Country	Greece
City	Kos
Period	02/07/2007 → 05/07/2007
Internet address	http://ecc07.ntua.gr/

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Cite this

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title = "Continuous-Discrete Time Prediction-Error Identification Relevant for Linear Model Predictive Control",

abstract = "A Prediction-error-method tailored for model based predictive control is presented. The prediction-error method studied are based on predictions using the Kalman filter and Kalman predictors for a linear discrete-time stochastic state space model. The linear discrete-time stochastic state space model is realized from a continuous-discrete-time linear stochastic system specified using transfer functions with time-delays. It is argued that the prediction-error criterion should be selected such that it is compatible with the objective function of the predictive controller in which the model is to be applied. The suitability of the proposed prediction error-method for predictive control is demonstrated for dual composition control of a simulated binary distillation column.",

author = "J{\o}rgensen, {John Bagterp} and J{\o}rgensen, {Sten Bay}",

year = "2007",

language = "English",

pages = "4752--4758",

booktitle = "Proceedings of the European Control Conference (ECC '07 )",

publisher = "IEEE",

address = "United States",

note = "European Control Conference 2007, ECC'07 ; Conference date: 02-07-2007 Through 05-07-2007",

url = "http://ecc07.ntua.gr/",

}

TY - GEN

T1 - Continuous-Discrete Time Prediction-Error Identification Relevant for Linear Model Predictive Control

AU - Jørgensen, John Bagterp

AU - Jørgensen, Sten Bay

PY - 2007

Y1 - 2007

N2 - A Prediction-error-method tailored for model based predictive control is presented. The prediction-error method studied are based on predictions using the Kalman filter and Kalman predictors for a linear discrete-time stochastic state space model. The linear discrete-time stochastic state space model is realized from a continuous-discrete-time linear stochastic system specified using transfer functions with time-delays. It is argued that the prediction-error criterion should be selected such that it is compatible with the objective function of the predictive controller in which the model is to be applied. The suitability of the proposed prediction error-method for predictive control is demonstrated for dual composition control of a simulated binary distillation column.

AB - A Prediction-error-method tailored for model based predictive control is presented. The prediction-error method studied are based on predictions using the Kalman filter and Kalman predictors for a linear discrete-time stochastic state space model. The linear discrete-time stochastic state space model is realized from a continuous-discrete-time linear stochastic system specified using transfer functions with time-delays. It is argued that the prediction-error criterion should be selected such that it is compatible with the objective function of the predictive controller in which the model is to be applied. The suitability of the proposed prediction error-method for predictive control is demonstrated for dual composition control of a simulated binary distillation column.

M3 - Article in proceedings

SP - 4752

EP - 4758

BT - Proceedings of the European Control Conference (ECC '07 )

PB - IEEE

T2 - European Control Conference 2007

Y2 - 2 July 2007 through 5 July 2007

ER -