Latent Variable MPC for trajectory tracking in batch processes: Role of the model structure

Author

Golshan, Masoud ; MacGregor, John F. ; Bruwer, Mark-John ; Mhaskar, Prashant

Author_Institution

Dept. of Chem. Eng., Mcmaster Univ., Hamilton, ON, Canada

fYear

2009

fDate

10-12 June 2009

Firstpage

4779

Lastpage

4784

Abstract

The multiphase latent variable model predictive control (MLV-MPC) is developed based on the principal component analysis (PCA) model. The proposed control methodology is capable of trajectory tracking as well as disturbance rejection. The model that is used in the course of MPC is a multiphase PCA model that is constructed based on the available data from the measurements on the process. Different data arrangements are studied and their effects on the performance of the control algorithm are evaluated.

Keywords

batch processing (industrial); position control; predictive control; principal component analysis; process control; tracking; batch process; latent variable MLV-MPC model; model predictive control; multiphase PCA model; multiphase latent variable; principal component analysis; trajectory tracking; Convergence; Helium; Monitoring; Mood; Predictive control; Predictive models; Principal component analysis; Process control; Trajectory; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, 2009. ACC '09.

Conference_Location

St. Louis, MO

ISSN

0743-1619

Print_ISBN

978-1-4244-4523-3

Electronic_ISBN

0743-1619

Type

conf

DOI

10.1109/ACC.2009.5160656

Filename

5160656