Intelligent modelling of MIMO nonlinear dynamic process plants for predictive control purposes

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

In this research, the input/output data of a MIMO nonlinear system are used to create intelligent models for nonlinear systems. Multi layer perceptrons and neuro-fuzzy networks are utilized for the intelligent models. To make these models suitable for the predictive control, a variety of subtle points should be considered. Recurrent models and subtractive clustering are used in this research, and a pre-processing is applied to the columns of the raw data. Then the prepared data are used to train models. A reliable checking process is also studied. A Catalytic Continuous Stirred Tank Reactor is used as a case study. A computer model is used to gather the input data rather than a real one. Finally, the simulation is successfully performed to indicate the capabilities of the intelligent modeling method as well as the importance of the design considerations offered in this paper.

Original languageEnglish
Title of host publicationProceedings of the 17th World Congress, International Federation of Automatic Control, IFAC
Volume17
Edition1 PART 1
DOIs
Publication statusPublished - 2008
Event17th World Congress, International Federation of Automatic Control, IFAC - Seoul, Korea, Republic of
Duration: Jul 6 2008Jul 11 2008

Other

Other17th World Congress, International Federation of Automatic Control, IFAC
CountryKorea, Republic of
CitySeoul
Period7/6/087/11/08

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Keywords

  • Identification for control
  • Iterative modelling and control design
  • Nonlinear system identification

ASJC Scopus subject areas

  • Control and Systems Engineering

Cite this

Mohammadzaheri, M., & Chen, L. (2008). Intelligent modelling of MIMO nonlinear dynamic process plants for predictive control purposes. In Proceedings of the 17th World Congress, International Federation of Automatic Control, IFAC (1 PART 1 ed., Vol. 17) https://doi.org/10.3182/20080706-5-KR-1001.0978