Volterra-Laguerre modeling for NMPC

School of Engineering

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

Published

Standard

Volterra-Laguerre modeling for NMPC. / Mahmoodi, Sanaz; Montazeri, Allahyar; Poshtan, Javad et al.
Signal Processing and Its Applications, 2007. ISSPA 2007. 9th International Symposium on. New York: IEEE, 2007. p. 1318-1321.

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

Harvard

Mahmoodi, S, Montazeri, A, Poshtan, J, Jahed-Motlagh, MR & Poshtan, M 2007, Volterra-Laguerre modeling for NMPC. in Signal Processing and Its Applications, 2007. ISSPA 2007. 9th International Symposium on. IEEE, New York, pp. 1318-1321, 9th International Symposium on Signal Processing and its Applications, Sharjah, 12/02/07. https://doi.org/10.1109/ISSPA.2007.4555604

APA

Mahmoodi, S., Montazeri, A., Poshtan, J., Jahed-Motlagh, M. R., & Poshtan, M. (2007). Volterra-Laguerre modeling for NMPC. In Signal Processing and Its Applications, 2007. ISSPA 2007. 9th International Symposium on (pp. 1318-1321). IEEE. https://doi.org/10.1109/ISSPA.2007.4555604

Vancouver

Mahmoodi S, Montazeri A, Poshtan J, Jahed-Motlagh MR, Poshtan M. Volterra-Laguerre modeling for NMPC. In Signal Processing and Its Applications, 2007. ISSPA 2007. 9th International Symposium on. New York: IEEE. 2007. p. 1318-1321 doi: 10.1109/ISSPA.2007.4555604

Author

Mahmoodi, Sanaz ; Montazeri, Allahyar ; Poshtan, Javad et al. / Volterra-Laguerre modeling for NMPC. Signal Processing and Its Applications, 2007. ISSPA 2007. 9th International Symposium on. New York : IEEE, 2007. pp. 1318-1321

Bibtex

@inproceedings{88214ebd5e574068aed5fc8ba5609be7,

title = "Volterra-Laguerre modeling for NMPC",

abstract = "Volterra series are perhaps the best understood nonlinear system representations in signal processing. They can be used to model a wide class of nonlinear systems. However, since these models are non-parsimonious in parameters, the symmetric kernel parameters are used This model is used to evaluate identification of a pH-neutralization process. The aim is to use this model in nonlinear model predictive control framework. For this purpose various orders of the Laguerre filters and also Volterra kernels are tested and the results are compared in terms of the validation of these models. The results show that to have a good trade off between simplicity of the model and its corresponding fitness, the selected nonlinear Volterra model has the memory of 3 while the number of its kennel is 4. The VAF of this model is 99.63% which is completely acceptable for nonlinear model predictive control applications.",

keywords = "PREDICTIVE CONTROL, IDENTIFICATION",

author = "Sanaz Mahmoodi and Allahyar Montazeri and Javad Poshtan and Jahed-Motlagh, {Mohammad Reza} and Majid Poshtan",

year = "2007",

doi = "10.1109/ISSPA.2007.4555604",

language = "English",

isbn = "978-1-4244-0778-1",

pages = "1318--1321",

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 - Volterra-Laguerre modeling for NMPC

AU - Mahmoodi, Sanaz

AU - Montazeri, Allahyar

AU - Poshtan, Javad

AU - Jahed-Motlagh, Mohammad Reza

AU - Poshtan, Majid

PY - 2007

Y1 - 2007

N2 - Volterra series are perhaps the best understood nonlinear system representations in signal processing. They can be used to model a wide class of nonlinear systems. However, since these models are non-parsimonious in parameters, the symmetric kernel parameters are used This model is used to evaluate identification of a pH-neutralization process. The aim is to use this model in nonlinear model predictive control framework. For this purpose various orders of the Laguerre filters and also Volterra kernels are tested and the results are compared in terms of the validation of these models. The results show that to have a good trade off between simplicity of the model and its corresponding fitness, the selected nonlinear Volterra model has the memory of 3 while the number of its kennel is 4. The VAF of this model is 99.63% which is completely acceptable for nonlinear model predictive control applications.

AB - Volterra series are perhaps the best understood nonlinear system representations in signal processing. They can be used to model a wide class of nonlinear systems. However, since these models are non-parsimonious in parameters, the symmetric kernel parameters are used This model is used to evaluate identification of a pH-neutralization process. The aim is to use this model in nonlinear model predictive control framework. For this purpose various orders of the Laguerre filters and also Volterra kernels are tested and the results are compared in terms of the validation of these models. The results show that to have a good trade off between simplicity of the model and its corresponding fitness, the selected nonlinear Volterra model has the memory of 3 while the number of its kennel is 4. The VAF of this model is 99.63% which is completely acceptable for nonlinear model predictive control applications.

KW - PREDICTIVE CONTROL

KW - IDENTIFICATION

U2 - 10.1109/ISSPA.2007.4555604

DO - 10.1109/ISSPA.2007.4555604

M3 - Conference contribution/Paper

SN - 978-1-4244-0778-1

SP - 1318

EP - 1321

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 -

Research

Associated organisational unit

Links

Text available via DOI:

Keywords