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Adaptive Volterra-Laguerre modeling for NMPC

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Adaptive Volterra-Laguerre modeling for NMPC. / Montazeri, Allahyar; Mahmoodi, Sanaz; Poshtan, Javad et al.
Signal Processing and Its Applications, 2007. ISSPA 2007. 9th International Symposium on. New York: IEEE, 2007. p. 1322-1325.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Montazeri, A, Mahmoodi, S, Poshtan, J, Poshtan, M & Jahed-Motlagh, M 2007, Adaptive Volterra-Laguerre modeling for NMPC. in Signal Processing and Its Applications, 2007. ISSPA 2007. 9th International Symposium on. IEEE, New York, pp. 1322-1325, 9th International Symposium on Signal Processing and its Applications, Sharjah, 12/02/07. https://doi.org/10.1109/ISSPA.2007.4555605

APA

Montazeri, A., Mahmoodi, S., Poshtan, J., Poshtan, M., & Jahed-Motlagh, M. (2007). Adaptive Volterra-Laguerre modeling for NMPC. In Signal Processing and Its Applications, 2007. ISSPA 2007. 9th International Symposium on (pp. 1322-1325). IEEE. https://doi.org/10.1109/ISSPA.2007.4555605

Vancouver

Montazeri A, Mahmoodi S, Poshtan J, Poshtan M, Jahed-Motlagh M. Adaptive Volterra-Laguerre modeling for NMPC. In Signal Processing and Its Applications, 2007. ISSPA 2007. 9th International Symposium on. New York: IEEE. 2007. p. 1322-1325 doi: 10.1109/ISSPA.2007.4555605

Author

Montazeri, Allahyar ; Mahmoodi, Sanaz ; Poshtan, Javad et al. / Adaptive Volterra-Laguerre modeling for NMPC. Signal Processing and Its Applications, 2007. ISSPA 2007. 9th International Symposium on. New York : IEEE, 2007. pp. 1322-1325

Bibtex

@inproceedings{232e8f3b269444feb6f1bd4ae62a8277,
title = "Adaptive Volterra-Laguerre modeling for NMPC",
abstract = "Model Predictive Control (MPC) is one of the most successful controllers in process industries. Process industries need a predictive controller that is low cost, easy to setup and maintains an adaptive behavior which accounts for plant changes, nonlinearities and under-modeling. To this aim, it is necessary to obtain a suitable adaptive modeling that can be easily used in nonlinear MPC framework. Experiments show performance advantages of Volterra series in terms of convergence, interpretability, and system sizes that can be handled They can be used to model a wide class of nonlinear systems. However, since these models are in general non-parsimonious in parameters, in this paper the symmetric kernel parameters and Laguerre filtering are used to generate regression vector. The performance of the proposed method is evaluated by simulation results obtained for identification experiments of a pH-neutralization process.",
keywords = "Predictive control, Identification",
author = "Allahyar Montazeri and Sanaz Mahmoodi and Javad Poshtan and Majid Poshtan and MohammadReza Jahed-Motlagh",
year = "2007",
doi = "10.1109/ISSPA.2007.4555605",
language = "English",
isbn = "978-1-4244-0778-1",
pages = "1322--1325",
booktitle = "Signal Processing and Its Applications, 2007. ISSPA 2007. 9th International Symposium on",
publisher = "IEEE",
note = "9th International Symposium on Signal Processing and its Applications ; Conference date: 12-02-2007 Through 15-02-2007",

}

RIS

TY - GEN

T1 - Adaptive Volterra-Laguerre modeling for NMPC

AU - Montazeri, Allahyar

AU - Mahmoodi, Sanaz

AU - Poshtan, Javad

AU - Poshtan, Majid

AU - Jahed-Motlagh, MohammadReza

PY - 2007

Y1 - 2007

N2 - Model Predictive Control (MPC) is one of the most successful controllers in process industries. Process industries need a predictive controller that is low cost, easy to setup and maintains an adaptive behavior which accounts for plant changes, nonlinearities and under-modeling. To this aim, it is necessary to obtain a suitable adaptive modeling that can be easily used in nonlinear MPC framework. Experiments show performance advantages of Volterra series in terms of convergence, interpretability, and system sizes that can be handled They can be used to model a wide class of nonlinear systems. However, since these models are in general non-parsimonious in parameters, in this paper the symmetric kernel parameters and Laguerre filtering are used to generate regression vector. The performance of the proposed method is evaluated by simulation results obtained for identification experiments of a pH-neutralization process.

AB - Model Predictive Control (MPC) is one of the most successful controllers in process industries. Process industries need a predictive controller that is low cost, easy to setup and maintains an adaptive behavior which accounts for plant changes, nonlinearities and under-modeling. To this aim, it is necessary to obtain a suitable adaptive modeling that can be easily used in nonlinear MPC framework. Experiments show performance advantages of Volterra series in terms of convergence, interpretability, and system sizes that can be handled They can be used to model a wide class of nonlinear systems. However, since these models are in general non-parsimonious in parameters, in this paper the symmetric kernel parameters and Laguerre filtering are used to generate regression vector. The performance of the proposed method is evaluated by simulation results obtained for identification experiments of a pH-neutralization process.

KW - Predictive control

KW - Identification

U2 - 10.1109/ISSPA.2007.4555605

DO - 10.1109/ISSPA.2007.4555605

M3 - Conference contribution/Paper

SN - 978-1-4244-0778-1

SP - 1322

EP - 1325

BT - Signal Processing and Its Applications, 2007. ISSPA 2007. 9th International Symposium on

PB - IEEE

CY - New York

T2 - 9th International Symposium on Signal Processing and its Applications

Y2 - 12 February 2007 through 15 February 2007

ER -