Fuzzy-neural predictive control using Levenberg-Marquardt optimization approach

Yancho Todorov, Margarita Terzyiska, Sevil Ahmed, Michail Petrov

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

1 Citation (Scopus)

Abstract

It is proposed in this paper a study on the influence of the Levenberg-Marquardt optimization approach for computation of the control actions in Nonlinear Model Predictive Controller. To predict the future plant behavior, a classical Takagi-Sugeno inference is used. A comparison by applying the Gradient descent and the Newton-Raphson optimization approaches is made. The efficiency of the proposed optimization strategies is demonstrated by experiments in MATLAB environment to control a Continuous Stirred Tank Reactor.

Original languageEnglish
Title of host publication2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013
PublisherInstitute of Electrical and Electronic Engineers IEEE
ISBN (Electronic)978-1-4799-0661-1, 978-1-4799-0660-4
ISBN (Print)978-1-4799-0659-8
DOIs
Publication statusPublished - 9 Sep 2013
MoE publication typeA4 Article in a conference publication
Event2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013 - Albena, Bulgaria
Duration: 19 Jun 201321 Jun 2013

Conference

Conference2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013
CountryBulgaria
CityAlbena
Period19/06/1321/06/13

Fingerprint

MATLAB
Controllers
Experiments

Keywords

  • Gradient descent
  • Levenberg- Marcquart
  • Newton-Raphson
  • Nonlinear Predictive Control
  • Optimization
  • Takagi-Sugeno model

Cite this

Todorov, Y., Terzyiska, M., Ahmed, S., & Petrov, M. (2013). Fuzzy-neural predictive control using Levenberg-Marquardt optimization approach. In 2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013 [6577624] Institute of Electrical and Electronic Engineers IEEE. https://doi.org/10.1109/INISTA.2013.6577624
Todorov, Yancho ; Terzyiska, Margarita ; Ahmed, Sevil ; Petrov, Michail. / Fuzzy-neural predictive control using Levenberg-Marquardt optimization approach. 2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013. Institute of Electrical and Electronic Engineers IEEE, 2013.
@inproceedings{1f084b82efae4d5ea3b9892333a9d57e,
title = "Fuzzy-neural predictive control using Levenberg-Marquardt optimization approach",
abstract = "It is proposed in this paper a study on the influence of the Levenberg-Marquardt optimization approach for computation of the control actions in Nonlinear Model Predictive Controller. To predict the future plant behavior, a classical Takagi-Sugeno inference is used. A comparison by applying the Gradient descent and the Newton-Raphson optimization approaches is made. The efficiency of the proposed optimization strategies is demonstrated by experiments in MATLAB environment to control a Continuous Stirred Tank Reactor.",
keywords = "Gradient descent, Levenberg- Marcquart, Newton-Raphson, Nonlinear Predictive Control, Optimization, Takagi-Sugeno model",
author = "Yancho Todorov and Margarita Terzyiska and Sevil Ahmed and Michail Petrov",
year = "2013",
month = "9",
day = "9",
doi = "10.1109/INISTA.2013.6577624",
language = "English",
isbn = "978-1-4799-0659-8",
booktitle = "2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013",
publisher = "Institute of Electrical and Electronic Engineers IEEE",
address = "United States",

}

Todorov, Y, Terzyiska, M, Ahmed, S & Petrov, M 2013, Fuzzy-neural predictive control using Levenberg-Marquardt optimization approach. in 2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013., 6577624, Institute of Electrical and Electronic Engineers IEEE, 2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013, Albena, Bulgaria, 19/06/13. https://doi.org/10.1109/INISTA.2013.6577624

Fuzzy-neural predictive control using Levenberg-Marquardt optimization approach. / Todorov, Yancho; Terzyiska, Margarita; Ahmed, Sevil; Petrov, Michail.

2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013. Institute of Electrical and Electronic Engineers IEEE, 2013. 6577624.

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

TY - GEN

T1 - Fuzzy-neural predictive control using Levenberg-Marquardt optimization approach

AU - Todorov, Yancho

AU - Terzyiska, Margarita

AU - Ahmed, Sevil

AU - Petrov, Michail

PY - 2013/9/9

Y1 - 2013/9/9

N2 - It is proposed in this paper a study on the influence of the Levenberg-Marquardt optimization approach for computation of the control actions in Nonlinear Model Predictive Controller. To predict the future plant behavior, a classical Takagi-Sugeno inference is used. A comparison by applying the Gradient descent and the Newton-Raphson optimization approaches is made. The efficiency of the proposed optimization strategies is demonstrated by experiments in MATLAB environment to control a Continuous Stirred Tank Reactor.

AB - It is proposed in this paper a study on the influence of the Levenberg-Marquardt optimization approach for computation of the control actions in Nonlinear Model Predictive Controller. To predict the future plant behavior, a classical Takagi-Sugeno inference is used. A comparison by applying the Gradient descent and the Newton-Raphson optimization approaches is made. The efficiency of the proposed optimization strategies is demonstrated by experiments in MATLAB environment to control a Continuous Stirred Tank Reactor.

KW - Gradient descent

KW - Levenberg- Marcquart

KW - Newton-Raphson

KW - Nonlinear Predictive Control

KW - Optimization

KW - Takagi-Sugeno model

UR - http://www.scopus.com/inward/record.url?scp=84883412287&partnerID=8YFLogxK

U2 - 10.1109/INISTA.2013.6577624

DO - 10.1109/INISTA.2013.6577624

M3 - Conference article in proceedings

AN - SCOPUS:84883412287

SN - 978-1-4799-0659-8

BT - 2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013

PB - Institute of Electrical and Electronic Engineers IEEE

ER -

Todorov Y, Terzyiska M, Ahmed S, Petrov M. Fuzzy-neural predictive control using Levenberg-Marquardt optimization approach. In 2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013. Institute of Electrical and Electronic Engineers IEEE. 2013. 6577624 https://doi.org/10.1109/INISTA.2013.6577624