Incorporating prior knowledge in fuzzy model identification

J. Abonyi, R. Babuška, H. B. Verbruggen, F. Szeifert

Research output: Contribution to journalArticle

45 Citations (Scopus)

Abstract

This paper presents an algorithm for incorporating a priori knowledge into data-driven identification of dynamic fuzzy models of the Takagi-Sugeno type. Knowledge about the modelled process such as its stability, minimal or maximal static gain, or the settling time of its step response can be translated into inequality constraints on the consequent parameters. By using input-output data, optimal parameter values are then found by means of quadratic programming. The proposed approach has been applied to the identification of a laboratory liquid level process. The obtained fuzzy model has been used in model-based predictive control. Real-time control results show that, when the proposed identification algorithm is applied, not only are physically justified models obtained but also the performance of the model-based controller improves with regard to the case where no prior knowledge is involved.

Original languageEnglish
Pages (from-to)657-667
Number of pages11
JournalInternational Journal of Systems Science
Volume31
Issue number5
DOIs
Publication statusPublished - Jan 1 2000

    Fingerprint

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications

Cite this