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 contributionScientificpeer-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
ISBN (Print)9781479906611
DOIs
Publication statusPublished - 2013
MoE publication typeA4 Article in a conference publication
EventInternational Symposium on Innovations in Intelligent Systems and Applications - Albena, Bulgaria
Duration: 19 Jun 201321 Jun 2013

Conference

ConferenceInternational Symposium on Innovations in Intelligent Systems and Applications
Abbreviated titleINISTA
CountryBulgaria
CityAlbena
Period19/06/201321/06/2013

Keywords

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

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