Convolution model based predictive controller for a nonlinear process

Arpad Bodizs, Ferenc Szeifert, Tibor Chovan

Research output: Contribution to journalArticle

9 Citations (Scopus)


The model predictive control (MPC) of a distributed parameter nonlinear laboratory heating system is studied. A nonlinear convolution model consisting of a linear dynamic and a nonlinear steady-state part is applied as the model of the process in the MPC algorithm. The dynamic part is represented by a relative impulse response model (IRM). The steady-state gain is derived from the first principle model of the system. The application of this special convolution model is as simple as the use of the transfer function model; however, it is valid on the whole operating range. MPC algorithms employing different models of the process are compared by simulation and physical tests.

Original languageEnglish
Pages (from-to)154-161
Number of pages8
JournalIndustrial and Engineering Chemistry Research
Issue number1
Publication statusPublished - Jan 1 1999


ASJC Scopus subject areas

  • Chemistry(all)
  • Chemical Engineering(all)
  • Industrial and Manufacturing Engineering

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