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Convolution model based predictive controller for a nonlinear process

delete1998-12-12
delete10
PRE
AI
Á
Árpád Bódizs *
F
Ferenc Szeifert
T
Tibor Chován
DOI：10.1021/ie980338qdelete
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Abstract

Abstract

En 中文
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.
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Journal

I
Industrial and Engineering Chemistry Research
IF:
3.9
Papers:
4.0W
Citations:
9.6W

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