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
PublisherIEEE 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
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

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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] IEEE Institute of Electrical and Electronic Engineers . https://doi.org/10.1109/INISTA.2013.6577624