Predictive Controller Based on Fuzzy-Neural model with multiple recurrent nodes

Margarita Terziyska, Yancho Todorov, Michail Petrov

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


A nonlinear predictive controller based on a recurrent neuro-fuzzy model is presented in this paper. Neuro-fuzzy model is realized with the T-S inference mechanism and includes global and local (after the rules layer) feedbacks. The proposed model is coupled with an optimization approach for computation of the control actions into a model based predictive controller. The efficiency of the proposed control policy is proved by simulation experiments to control a Continuous Stirred Tank Reactor (CSTR).
Original languageEnglish
Title of host publicationInternational Conference "FOOD SCIENCE, ENGINEERING AND TECHNOLOGIES – 2013“, 18-19 October 2013, Plovdiv
Publication statusPublished - 2013
MoE publication typeA4 Article in a conference publication
EventFood Science, Engineering and Technologies - Plovdiv, Bulgaria
Duration: 18 Oct 201319 Oct 2013


ConferenceFood Science, Engineering and Technologies


  • process control
  • artificial intelligence
  • fuzzy systems
  • neural networks
  • optimizaiton

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