Methodology for Detecting Model-Plant Mismatches Affecting Model Predictive Control Performance

被引：33

作者：

Botelho, Viviane ^{[1
]}

Trierweiler, Jorge Otavio ^{[1
]}

Farenzena, Marcelo ^{[1
]}

Duraiski, Ricardo ^{[2
]}

机构：

[1] Univ Fed Rio Grande do Sul, Dept Chem Engn, Grp Intensificat Modeling Simulat Control & Opt P, BR-90040040 Porto Alegre, RS, Brazil

[2] Trisolut Engn Solut Ltd, BR-90010080 Porto Alegre, RS, Brazil

来源：

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH | 2015年 / 54卷 / 48期

关键词：

Predictive control systems;

D O I：

10.1021/acs.iecr.5b01967

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

The model quality for a model predictive control (MPG) is critical for the control loop performance. Thus, assessing the effect of model plant mismatch (MPM) is fundamental for performance assessment and monitoring the MPG. This paper proposes a method for evaluating model quality based on the investigation of closed-loop data and the nominal output sensitivity function, which facilitates the assessment procedure for the actual closed-loop performances. The effectiveness of the proposed method is illustrated by a multivariable case study, considering linear and nonlinear plants.

引用

页码：12072 / 12085

页数：14

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