<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

Supervisory predictive control and on-line set-point optimization

Institute of Control and Computation Engineering, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland

International Journal of Applied Mathematics and Computer Science

{"article-title":"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...

Supervisory predictive control and on-line set-point optimization

Publié en ligne: 27 sept. 2010

Pages: 483 - 495

DOI: https://doi.org/10.2478/v10006-010-0035-1

Mots clés
predictive control, nonlinear control, linearization, model uncertainty, constrained control, set-point optimization

This content is open access.

Supervisory predictive control and on-line set-point optimization

Publié en ligne: 27 sept. 2010

Pages: 483 - 495

DOI: https://doi.org/10.2478/v10006-010-0035-1

Mots cléspredictive control, nonlinear control, linearization, model uncertainty, constrained control, set-point optimization

This content is open access.

Mots clés
predictive control, nonlinear control, linearization, model uncertainty, constrained control, set-point optimization