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

共 11 条

[1] Quantifying the impact of model-plant mismatch on controller performance
Badwe, Abhijit S.
Patwardhan, Rohit S.
Shah, Sirish L.
Patwardhan, Sachin C.
Gudi, Ravindra D.
[J]. JOURNAL OF PROCESS CONTROL, 2010, 20 (04) : 408 - 425
[2] Detection of model-plant mismatch in MPC applications
Badwe, Abhijit S.
Gudi, Ravindra D.
Patwardhan, Rohit S.
Shah, Sirish L.
Patwardhan, Sachin C.
[J]. JOURNAL OF PROCESS CONTROL, 2009, 19 (08) : 1305 - 1313
[3] Assessing the need for process re-identification
Conner, JS
Seborg, DE
[J]. INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2005, 44 (08) : 2767 - 2775
[4] Model predictive control relevant identification and validation
Huang, B
Malhotra, A
Tamayo, EC
[J]. CHEMICAL ENGINEERING SCIENCE, 2003, 58 (11) : 2389 - 2401
[5] A method of MPC model error detection
Ji, Guoli
Zhang, Kangkang
Zhu, Yucai
[J]. JOURNAL OF PROCESS CONTROL, 2012, 22 (03) : 635 - 642
[6] Model analysis and performance analysis of two industrial MPCs
Jiang, Hailei
Shah, Sirish L.
Huang, Biao
Wilson, Bruce
Patwardhan, Rohit
Szeto, Foon
[J]. CONTROL ENGINEERING PRACTICE, 2012, 20 (03) : 219 - 235
[7] Kano M., 2010, P 9 INT S DYN CONTR
[8] Kempf A., 2003, THESIS
[9] Ljung L., 1999, System Identification: Theory for the User, V2nd
[10] Skogestad Sigurd, 2005, Multivariable feedback control: analysis and design

← 1 2 →