PLS and its application within model predictive controllers

Awad Shamekh; Barry Lennox; David Sandoz; Ognjen Marjanovic

doi:10.3182/20080706-5-KR-1001.0378

PLS and its application within model predictive controllers

Awad Shamekh, Barry Lennox, David Sandoz, Ognjen Marjanovic

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

Abstract

Many system identification techniques have been proposed over the last few decades, including ordinary and recursive least squares. Recently, Partial Least Squares (PLS) has become a popular tool in the chemometric community and is beginning to be applied to solve complex industrial process control problems. These studies have tended to ignore the issue of bias with this form of model and it is this issue that is addressed in this article. The paper describes the development of an unbiased recursive PLS algorithm that is successfully applied to two simulated processes. Copyright © 2007 International Federation of Automatic Control All Rights Reserved.

Original language	English
Title of host publication	IFAC Proceedings Volumes (IFAC-PapersOnline)\|IFAC Proc. Vol. (IFAC-PapersOnline)
Volume	17
DOIs	https://doi.org/10.3182/20080706-5-KR-1001.0378
Publication status	Published - 2008
Event	17th World Congress, International Federation of Automatic Control, IFAC - Seoul Duration: 1 Jul 2008 → … http://www.ifac-papersonline.net/Detailed/37247.html

Conference

Conference	17th World Congress, International Federation of Automatic Control, IFAC
City	Seoul
Period	1/07/08 → …
Internet address	http://www.ifac-papersonline.net/Detailed/37247.html

Keywords

Identification for control
Recursive identification
Subspace methods

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.3182/20080706-5-KR-1001.0378

Cite this

@inproceedings{0527012dd9214fc1a3599af03f1e89a1,

title = "PLS and its application within model predictive controllers",

abstract = "Many system identification techniques have been proposed over the last few decades, including ordinary and recursive least squares. Recently, Partial Least Squares (PLS) has become a popular tool in the chemometric community and is beginning to be applied to solve complex industrial process control problems. These studies have tended to ignore the issue of bias with this form of model and it is this issue that is addressed in this article. The paper describes the development of an unbiased recursive PLS algorithm that is successfully applied to two simulated processes. Copyright {\textcopyright} 2007 International Federation of Automatic Control All Rights Reserved.",

keywords = "Identification for control, Recursive identification, Subspace methods",

author = "Awad Shamekh and Barry Lennox and David Sandoz and Ognjen Marjanovic",

year = "2008",

doi = "10.3182/20080706-5-KR-1001.0378",

language = "English",

isbn = "9783902661005",

volume = "17",

booktitle = "IFAC Proceedings Volumes (IFAC-PapersOnline)|IFAC Proc. Vol. (IFAC-PapersOnline)",

note = "17th World Congress, International Federation of Automatic Control, IFAC ; Conference date: 01-07-2008",

url = "http://www.ifac-papersonline.net/Detailed/37247.html",

}

TY - GEN

T1 - PLS and its application within model predictive controllers

AU - Shamekh, Awad

AU - Lennox, Barry

AU - Sandoz, David

AU - Marjanovic, Ognjen

PY - 2008

Y1 - 2008

N2 - Many system identification techniques have been proposed over the last few decades, including ordinary and recursive least squares. Recently, Partial Least Squares (PLS) has become a popular tool in the chemometric community and is beginning to be applied to solve complex industrial process control problems. These studies have tended to ignore the issue of bias with this form of model and it is this issue that is addressed in this article. The paper describes the development of an unbiased recursive PLS algorithm that is successfully applied to two simulated processes. Copyright © 2007 International Federation of Automatic Control All Rights Reserved.

AB - Many system identification techniques have been proposed over the last few decades, including ordinary and recursive least squares. Recently, Partial Least Squares (PLS) has become a popular tool in the chemometric community and is beginning to be applied to solve complex industrial process control problems. These studies have tended to ignore the issue of bias with this form of model and it is this issue that is addressed in this article. The paper describes the development of an unbiased recursive PLS algorithm that is successfully applied to two simulated processes. Copyright © 2007 International Federation of Automatic Control All Rights Reserved.

KW - Identification for control

KW - Recursive identification

KW - Subspace methods

U2 - 10.3182/20080706-5-KR-1001.0378

DO - 10.3182/20080706-5-KR-1001.0378

M3 - Conference contribution

SN - 9783902661005

VL - 17

BT - IFAC Proceedings Volumes (IFAC-PapersOnline)|IFAC Proc. Vol. (IFAC-PapersOnline)

T2 - 17th World Congress, International Federation of Automatic Control, IFAC

Y2 - 1 July 2008

ER -

PLS and its application within model predictive controllers

Abstract

Conference

Keywords

UN SDGs

Access to Document

Fingerprint

Cite this