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State-Space Fuzzy-Neural Predictive Control

  • Yancho TodorovEmail author
  • Margarita Terziyska
  • Michail Petrov
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 657)

Abstract

The purpose of this work is to give an idea about the available potentials of state-space predictive control methodology based on fuzzy-neural modeling technique and different optimization procedures for process control. The proposed controller methodologies are based on Fuzzy-Neural State-Space Hammerstein model and variants of Quadratic Programming optimization algorithms. The effects of the proposed approaches are studied by simulation experiments to control a primary drying cycle in small-scale freeze-drying plant. The obtained results show a well-driven drying process without violation of the system constraints and accurate minimum error model prediction of the considered system states and output.

Keywords

Model Predictive Control Nonlinear Model Predictive Control Optimal Control Policy Model Predictive Control Algorithm Sublimation Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Yancho Todorov
    • 1
    Email author
  • Margarita Terziyska
    • 2
  • Michail Petrov
    • 3
  1. 1.Department of “Intelligent Systems”Institute of Information and Communication Technologies, Bulgarian Academy of SciencesSofiaBulgaria
  2. 2.Department of “Informatics and Statistics”University of Food TechnologiesPlovdivBulgaria
  3. 3.Department of “Control Systems”Technical Univeristy- Sofia, branch PlovdivPlovdivBulgaria

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