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Application of evolutionary learning in Wiener neural identification and predictive control of a plug-flow tubular reactor

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Application of evolutionary learning in Wiener neural identification and predictive control of a plug-flow tubular reactor. / Arefi, Mohammad Mehdi; Montazeri, Allahyar; Jahed-Motlagh, Mohanimad Reza et al.
Industrial Electronics Society, 2007. IECON 2007. 33rd Annual Conference of the IEEE. New York: IEEE, 2007. p. 644-650 (IEEE Industrial Electronics Society).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Arefi, MM, Montazeri, A, Jahed-Motlagh, MR & Poshtan, J 2007, Application of evolutionary learning in Wiener neural identification and predictive control of a plug-flow tubular reactor. in Industrial Electronics Society, 2007. IECON 2007. 33rd Annual Conference of the IEEE. IEEE Industrial Electronics Society, IEEE, New York, pp. 644-650, 33rd Annual Conference of the IEEE-Industrial-Electronics-Society, Taipei, 5/11/07. https://doi.org/10.1109/IECON.2007.4460273

APA

Arefi, M. M., Montazeri, A., Jahed-Motlagh, M. R., & Poshtan, J. (2007). Application of evolutionary learning in Wiener neural identification and predictive control of a plug-flow tubular reactor. In Industrial Electronics Society, 2007. IECON 2007. 33rd Annual Conference of the IEEE (pp. 644-650). (IEEE Industrial Electronics Society). IEEE. https://doi.org/10.1109/IECON.2007.4460273

Vancouver

Arefi MM, Montazeri A, Jahed-Motlagh MR, Poshtan J. Application of evolutionary learning in Wiener neural identification and predictive control of a plug-flow tubular reactor. In Industrial Electronics Society, 2007. IECON 2007. 33rd Annual Conference of the IEEE. New York: IEEE. 2007. p. 644-650. (IEEE Industrial Electronics Society). doi: 10.1109/IECON.2007.4460273

Author

Arefi, Mohammad Mehdi ; Montazeri, Allahyar ; Jahed-Motlagh, Mohanimad Reza et al. / Application of evolutionary learning in Wiener neural identification and predictive control of a plug-flow tubular reactor. Industrial Electronics Society, 2007. IECON 2007. 33rd Annual Conference of the IEEE. New York : IEEE, 2007. pp. 644-650 (IEEE Industrial Electronics Society).

Bibtex

@inproceedings{c09ea036c9a24f94a9f3c8bc78191c61,
title = "Application of evolutionary learning in Wiener neural identification and predictive control of a plug-flow tubular reactor",
abstract = "In this paper, identification and nonlinear model predictive control of highly nonlinear plug-flow tubular reactor based on Wiener model is studied. This process simulated in a rather realistic environment by HYSYS, and the obtained data is in connection with MATLAB for identification and control purpose. The process is identified with NN-Wiener identification method, and two linear and nonlinear model predictive controllers are applied with the ability of rejecting slowly varying unmeasured disturbance. Since the identification problem must be solved with a nonlinear optimization method, to attain the best possible model for prediction genetic algorithm is used. The Simulation results show that the obtained Wiener model has a good capability to predict the step response of the process. The results for control are also compared with a common PI controller for temperature control of tubular reactor. It is shown that the nonlinear controller has the fastest damped response in comparison with the other two controllers.",
keywords = "SYSTEMS, STATE, INTERNAL MODEL CONTROL",
author = "Arefi, {Mohammad Mehdi} and Allahyar Montazeri and Jahed-Motlagh, {Mohanimad Reza} and Javad Poshtan",
year = "2007",
doi = "10.1109/IECON.2007.4460273",
language = "English",
isbn = "978-1-4244-0783-5",
series = "IEEE Industrial Electronics Society",
publisher = "IEEE",
pages = "644--650",
booktitle = "Industrial Electronics Society, 2007. IECON 2007. 33rd Annual Conference of the IEEE",
note = "33rd Annual Conference of the IEEE-Industrial-Electronics-Society ; Conference date: 05-11-2007 Through 08-11-2007",

}

RIS

TY - GEN

T1 - Application of evolutionary learning in Wiener neural identification and predictive control of a plug-flow tubular reactor

AU - Arefi, Mohammad Mehdi

AU - Montazeri, Allahyar

AU - Jahed-Motlagh, Mohanimad Reza

AU - Poshtan, Javad

PY - 2007

Y1 - 2007

N2 - In this paper, identification and nonlinear model predictive control of highly nonlinear plug-flow tubular reactor based on Wiener model is studied. This process simulated in a rather realistic environment by HYSYS, and the obtained data is in connection with MATLAB for identification and control purpose. The process is identified with NN-Wiener identification method, and two linear and nonlinear model predictive controllers are applied with the ability of rejecting slowly varying unmeasured disturbance. Since the identification problem must be solved with a nonlinear optimization method, to attain the best possible model for prediction genetic algorithm is used. The Simulation results show that the obtained Wiener model has a good capability to predict the step response of the process. The results for control are also compared with a common PI controller for temperature control of tubular reactor. It is shown that the nonlinear controller has the fastest damped response in comparison with the other two controllers.

AB - In this paper, identification and nonlinear model predictive control of highly nonlinear plug-flow tubular reactor based on Wiener model is studied. This process simulated in a rather realistic environment by HYSYS, and the obtained data is in connection with MATLAB for identification and control purpose. The process is identified with NN-Wiener identification method, and two linear and nonlinear model predictive controllers are applied with the ability of rejecting slowly varying unmeasured disturbance. Since the identification problem must be solved with a nonlinear optimization method, to attain the best possible model for prediction genetic algorithm is used. The Simulation results show that the obtained Wiener model has a good capability to predict the step response of the process. The results for control are also compared with a common PI controller for temperature control of tubular reactor. It is shown that the nonlinear controller has the fastest damped response in comparison with the other two controllers.

KW - SYSTEMS

KW - STATE

KW - INTERNAL MODEL CONTROL

U2 - 10.1109/IECON.2007.4460273

DO - 10.1109/IECON.2007.4460273

M3 - Conference contribution/Paper

SN - 978-1-4244-0783-5

T3 - IEEE Industrial Electronics Society

SP - 644

EP - 650

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

PB - IEEE

CY - New York

T2 - 33rd Annual Conference of the IEEE-Industrial-Electronics-Society

Y2 - 5 November 2007 through 8 November 2007

ER -