Identification and control of nonlinear systems using Fuzzy Hammerstein models

J. Abonyi, R. Babuška, M. Ayala Botto, F. Szeifert, L. Nagy

Research output: Contribution to journalArticle

61 Citations (Scopus)

Abstract

This paper addresses the identification and control of nonlinear systems by means of Fuzzy Hammerstein (FH) models, which consist of a static fuzzy model connected in series with a linear dynamic model. For the identification of nonlinear dynamic systems with the proposed FH models, two methods are proposed. The first one is an alternating optimization algorithm that iteratively refines the estimate of the linear dynamics and the parameters of the static fuzzy model. The second method estimates the parameters of the nonlinear static model and of the linear dynamic model simultaneously by using a constrained recursive least-squares algorithm. The obtained FH model is incorporated in a model-based predictive control scheme and a new constraint-handling method is presented. A simulated water-heater process is used as an illustrative example. A comparison with an affine neural network and a linear model is given. Simulation results show that the proposed FH modeling approach is useful for modular parsimonious modeling and model-based control of nonlinear systems.

Original languageEnglish
Pages (from-to)4302-4314
Number of pages13
JournalIndustrial and Engineering Chemistry Research
Volume39
Issue number11
Publication statusPublished - Nov 2000

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Nonlinear systems
Identification (control systems)
Dynamic models
Water heaters
Dynamical systems
modeling
Neural networks

ASJC Scopus subject areas

  • Polymers and Plastics
  • Environmental Science(all)
  • Chemical Engineering (miscellaneous)

Cite this

Identification and control of nonlinear systems using Fuzzy Hammerstein models. / Abonyi, J.; Babuška, R.; Ayala Botto, M.; Szeifert, F.; Nagy, L.

In: Industrial and Engineering Chemistry Research, Vol. 39, No. 11, 11.2000, p. 4302-4314.

Research output: Contribution to journalArticle

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