Assessment of Model-Plant Mismatch by the Nominal Sensitivity Function for Unconstrained MPC

被引：5

作者：

Botelho, Viviane Rodrigues ^{[1
]}

Trierweiler, Jorge Otavio ^{[1
]}

Farenzena, Marcelo ^{[1
]}

Duraiski, Ricardo ^{[2
]}

机构：

[1] Univ Fed Rio Grande do Sul, GIMSCOP Grp Intensificat Modeling Simulat Control, Dept Chem Engn, R Engn Luis Englert S-N,Campus Cent, BR-90040040 Porto Alegre, RS, Brazil

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

来源：

IFAC PAPERSONLINE | 2015年 / 48卷 / 08期

关键词：

Model Predictive Control; Model Plant Mismatch; Sensitivity Function; Control Performance Assessment;

D O I：

10.1016/j.ifacol.2015.09.059

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Model Predictive Control (MPC) is a class of control systems which use a dynamic process model to predict the best future control actions based on past information. Thus, a representative process model is a key factor for its correct performance. Therefore, the investigation of model-plant-mismatch effect is very important issue for MPC performance assessment, monitoring, and diagnosis. This paper presents a method for model quality evaluation based on the investigation of closed-loop data and the nominal complementary sensitivity function. The proposed approach ensures that the MPC tuning is taken into account in the evaluation of the model quality. A SISO case study is analyzed and the results show the effectiveness of the method. (C) 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd All rights reserved.

引用

页码：753 / 758

页数：6

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