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Efficient Nonlinear Model Predictive Control
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Author(s):
R. Findeisen, F. Allgöwer, M. Diehl, H.G. Bock, J.P.Schlöder,
Z. Nagy
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Publication Info:
In Proceedings of Chemical Process Control 6, CPC 6, 2001, pp. 454-460, Tuscon,
Arizona, USA
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Abstract:
The growing interest in model predictive
control for nonlinear systems is motivated by the fact that today's
processes need to be operated under tighter performance
specifications to guarantee profitable and environmentally safe
production. Nonlinear model predictive control (NMPC)
does allow the direct consideration of a nonlinear process model as
well as state and input constraints. Thus NMPC seems to be well
suited for these kind of processes. Many theoretical issues in NMPC
have been attacked and solved in recent years. Despite this
progress there are a number of problems that have to be solved before
NMPC can be applied in practice. One essential problem
is the high on-line computational load,
since at each sampling instant a nonlinear optimal control
problem has to be solved. In this paper, we summarize recent results from
a case study showing the practical applicability of NMPC for process
control. Especially, we discuss how recent
advances in NMPC theory and dynamic optimization can
be used, such that the real-time application of NMPC becomes feasible
even for high dimensional problems. As
application example we consider the real-time control of a high
purity distillation column.
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Date:
January 2001
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Type of Publication:
Internal Report 2001-1
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Publisher/Supervisor:
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File Download:
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Universität Stuttgart