Fuzzy modeling with multivariate membership functions: Gray-box identification and control design

J. Abonyi, Robert Babuška, F. Szeifert

Research output: Contribution to journalArticle

43 Citations (Scopus)

Abstract

A novel framework for fuzzy modeling and model-based control design is described. The fuzzy model is of the Takagi-Sugeno (TS) type with constant consequents. It uses multivariate antecedent membership functions obtained by Delaunay triangulation of their characteristic points. The number and position of these points are determined by an iterative insertion algorithm. Constrained optimization is used to estimate the consequent parameters, where the constraints are based on control-relevant a priori knowledge about the modeled process. Finally, methods for control design through linearization and inversion of this model are developed. The proposed techniques are demonstrated by means of two benchmark examples: identification of the well-known Box-Jenkins gas furnace and inverse model-based control of a pH process. The obtained results are compared with results from the literature.

Original languageEnglish
Pages (from-to)755-767
Number of pages13
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume31
Issue number5
DOIs
Publication statusPublished - Oct 2001

Fingerprint

Membership functions
Gas furnaces
Constrained optimization
Triangulation
Linearization

Keywords

  • A priori knowledge
  • Delaunay triangulation
  • Fuzzy modeling
  • Gray-box identification
  • Inverse control
  • Model-based control

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence
  • Human-Computer Interaction

Cite this

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