Inverse-model predictive control based on INFUMO-BB-BC optimization

Simon Oblak, Tufan Kumbasar, Igor Škrjanc, Engin Yeşil

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

7 Citations (Scopus)

Abstract

This paper presents a method of advanced predictive control combining a contemporary optimization procedure Big Bang - Big Crunch (BB-BC) and the use of an interval fuzzy model (INFUMO). The method is founded on the inverse-fuzzy-model-based controller where adaptation of the model to the process is done in each calculation step. The INFUMO was used to identify a band of possible process parameter values and hence to limit the optimization search space, which resulted in a considerable reduction of computational load. The benefits of the method were illustrated on a simulation model with high-grade uncertainties and rapid changes of parameters.

Original languageEnglish
Title of host publicationIFAC International Workshop on the Adaptation and Learning in Control and Signal Processing, ALCOSP 2010 - Proceedings
PublisherIFAC Secretariat
Pages294-299
Number of pages6
EditionPART 1
ISBN (Print)9783902661852
DOIs
Publication statusPublished - 2010
Event10th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing, ALCOSP 2010 - Antalya, Turkey
Duration: 26 Aug 201028 Aug 2010

Publication series

NameIFAC Proceedings Volumes (IFAC-PapersOnline)
NumberPART 1
Volume1
ISSN (Print)1474-6670

Conference

Conference10th IFAC International Workshop on Adaptation and Learning in Control and Signal Processing, ALCOSP 2010
Country/TerritoryTurkey
CityAntalya
Period26/08/1028/08/10

Funding

⋆ The authors would like to thank to Ministry of School, Science and Sports of Republic of Slovenia and Scientific and Technological Research Council of Turkey (TUBITAK) for support

FundersFunder number
Ministry of School, Science and Sports of Republic of Slovenia and Scientific and Technological Research Council of Turkey
TUBITAK

    Keywords

    • Genetic algorithms
    • Model-based control
    • Nonlinear control systems
    • Uncertain dynamic systems

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