State-space fuzzy-neural predictive control

Yancho Todorov, Margarita Terziyska, Michail Petrov

Research output: Chapter in Book/Report/Conference proceedingChapter or book articleScientificpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationStudies in Computational Intelligence
PublisherSpringer
Pages291-312
Number of pages22
DOIs
Publication statusPublished - 1 Jan 2017
MoE publication typeA3 Part of a book or another research book

Publication series

SeriesStudies in Computational Intelligence
Volume657
ISSN1860-949X

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  • Cite this

    Todorov, Y., Terziyska, M., & Petrov, M. (2017). State-space fuzzy-neural predictive control. In Studies in Computational Intelligence (pp. 291-312). Springer. Studies in Computational Intelligence, Vol.. 657 https://doi.org/10.1007/978-3-319-41438-6_17