Model predictive control relevant identification and validation

被引：38

作者：

Huang, B ^{[1
]}

Malhotra, A

Tamayo, EC

机构：

[1] Univ Alberta, Dept Chem & Mat Engn, Edmonton, AB T6G 2G6, Canada

[2] Syncrude Canada Ltd, Ft McMurray, AB T9G 2G6, Canada

来源：

CHEMICAL ENGINEERING SCIENCE | 2003年 / 58卷 / 11期

关键词：

model validation; detection of abrupt change; process identification; optimal prediction; prediction error method; model predictive control;

D O I：

10.1016/S0009-2509(03)00077-0

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

The role of data prefiltering in model identification and validation is presented in this paper. A model predictive control relevant data prefilter, namely the multistep ahead prediction filter for optimal predictions over every step within a finite horizon, is presented. It is shown that models that minimize the multistep prediction errors can be identified or verified by filtering the data using certain data prefilters and then applying the prediction error method to the filtered data. Based on these identification results, a predictive control relevant model validation scheme using the local approach is proposed. The developed algorithms are verified through simulations as well as industrial applications. (C) 2003 Elsevier Science Ltd. All rights reserved.

引用

页码：2389 / 2401

页数：13

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