Supervisory predictive control and on-line set-point optimization

The subject of this paper is to discuss selected effective known and novel structures for advanced process control and optimization. The role and techniques of model-based predictive control (MPC) in a supervisory (advanced) control layer are first shortly discussed. The emphasis is put on algorithm...

Full description

Saved in:
Bibliographic Details
Published in:International Journal of Applied Mathematics and Computer Science Vol. 20; no. 3; pp. 483 - 495
Main Author: Tatjewski, Piotr
Format: Journal Article
Language:English
Published: Zielona Góra Versita 01.09.2010
De Gruyter Brill Sp. z o.o., Paradigm Publishing Services
Subjects:
constrained control
linearization
model uncertainty
nonlinear control
predictive control
set-point optimization
ISSN:1641-876X, 2083-8492
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The subject of this paper is to discuss selected effective known and novel structures for advanced process control and optimization. The role and techniques of model-based predictive control (MPC) in a supervisory (advanced) control layer are first shortly discussed. The emphasis is put on algorithm efficiency for nonlinear processes and on treating uncertainty in process models, with two solutions presented: the structure of nonlinear prediction and successive linearizations for nonlinear control, and a novel algorithm based on fast model selection to cope with process uncertainty. Issues of cooperation between MPC algorithms and on-line steady-state set-point optimization are next discussed, including integrated approaches. Finally, a recently developed two-purpose supervisory predictive set-point optimizer is discussed, designed to perform simultaneously two goals: economic optimization and constraints handling for the underlying unconstrained direct controllers.
Bibliography:ArticleID:v10006-010-0035-1
v10006-010-0035-1.pdf
ark:/67375/QT4-Z013CZFQ-T
istex:771E91FC3FCACCBB666CB2B45E5F4BD892B9096B
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1641-876X
2083-8492
DOI:10.2478/v10006-010-0035-1