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...

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Veröffentlicht in:International Journal of Applied Mathematics and Computer Science Jg. 20; H. 3; S. 483 - 495
1. Verfasser: Tatjewski, Piotr
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Zielona Góra Versita 01.09.2010
De Gruyter Brill Sp. z o.o., Paradigm Publishing Services
Schlagworte:
constrained control
linearization
model uncertainty
nonlinear control
predictive control
set-point optimization
ISSN:1641-876X, 2083-8492
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Zusammenfassung: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.
Bibliographie:ArticleID:v10006-010-0035-1
v10006-010-0035-1.pdf
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ISSN:1641-876X
2083-8492
DOI:10.2478/v10006-010-0035-1