Fuzzy-neural predictive control using Levenberg-Marquardt optimization approach

Yancho Todorov; Margarita Terzyiska; Sevil Ahmed; Michail Petrov

doi:10.1109/INISTA.2013.6577624

Fuzzy-neural predictive control using Levenberg-Marquardt optimization approach

Yancho Todorov
, Margarita Terzyiska
, Sevil Ahmed
, Michail Petrov

Not published at VTT

Bulgarian Academy of Sciences
Technical University of Sofia

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

3 Citations (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 language	English
Title of host publication	2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013
Publisher	IEEE Institute of Electrical and Electronic Engineers
ISBN (Electronic)	978-1-4799-0661-1, 978-1-4799-0660-4
ISBN (Print)	978-1-4799-0659-8
DOIs	https://doi.org/10.1109/INISTA.2013.6577624
Publication status	Published - 9 Sept 2013
MoE publication type	A4 Article in a conference publication
Event	2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013 - Albena, Bulgaria Duration: 19 Jun 2013 → 21 Jun 2013

Conference

Conference	2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013
Country/Territory	Bulgaria
City	Albena
Period	19/06/13 → 21/06/13

Keywords

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

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10.1109/INISTA.2013.6577624

Cite this

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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 = sep,

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 = "IEEE Institute of Electrical and Electronic Engineers",

address = "United States",

note = "2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013 ; Conference date: 19-06-2013 Through 21-06-2013",

}

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

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-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 - https://www.scopus.com/pages/publications/84883412287

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 - IEEE Institute of Electrical and Electronic Engineers

T2 - 2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013

Y2 - 19 June 2013 through 21 June 2013

ER -

Fuzzy-neural predictive control using Levenberg-Marquardt optimization approach

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Keywords

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