International Journal of applied mathematics and computer science

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Paper details

Number 3 - September 2010
Volume 20 - 2010

Supervisory predictive control and on-line set-point optimization

Piotr Tatjewski

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.

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

DOI
10.2478/v10006-010-0035-1