Multiobjective optimization using variable complexity modelling for control system design

Valceres V R Silva, Peter J. Fleming, Jungiro Sugimoto, Ryuichi Yokoyama

    Research output: Contribution to journalArticle

    22 Citations (Scopus)

    Abstract

    A multi-stage design approach that uses a multiobjective genetic algorithm as the framework for optimization and multiobjective preference articulation, and an H_infty loop-shaping technique are used to design controllers for a gas turbine engine. A non-linear model is used to assess performance of the controller. Because the computational load of applying multiobjective genetic algorithm to this control strategy is very high, a neural network and response surface models are used in order to speed up the design process within the framework of a multiobjective genetic algorithm. The final designs are checked using the original non-linear model.

    Original languageEnglish
    Pages (from-to)392-401
    Number of pages10
    JournalApplied Soft Computing Journal
    Volume8
    Issue number1
    DOIs
    Publication statusPublished - 2008 Jan

    Fingerprint

    Multiobjective optimization
    Systems analysis
    Control systems
    Genetic algorithms
    Controllers
    Gas turbines
    Turbines
    Neural networks

    Keywords

    • H_infty control
    • Multiobjective genetic algorithms
    • Neural networks
    • Optimization
    • Variable complexity modelling

    ASJC Scopus subject areas

    • Software
    • Computer Science Applications
    • Artificial Intelligence

    Cite this

    Multiobjective optimization using variable complexity modelling for control system design. / Silva, Valceres V R; Fleming, Peter J.; Sugimoto, Jungiro; Yokoyama, Ryuichi.

    In: Applied Soft Computing Journal, Vol. 8, No. 1, 01.2008, p. 392-401.

    Research output: Contribution to journalArticle

    Silva, Valceres V R ; Fleming, Peter J. ; Sugimoto, Jungiro ; Yokoyama, Ryuichi. / Multiobjective optimization using variable complexity modelling for control system design. In: Applied Soft Computing Journal. 2008 ; Vol. 8, No. 1. pp. 392-401.
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