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Efficient Nonlinear Model Predictive Control for Large Scale Constrained Processes
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Author(s):
F. Allgöwer, R. Findeisen, Z. Nagy, M. Diehl, H.G. Bock, J.P. Schlöder
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Publication Info:
In proceedings of the Sixth International Conference on Methods
and Models in Automation and Robotics, MMAR 2000, pp 43-54,
Miedzyzdroje, Poland
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Abstract:
In the past decade the field of nonlinear model
predictive control (NMPC) has witnessed steadily increasing
attention from control practitioners. Its popularity comes from
the fact that today's processes need to be operated under much
tighter performance specifications while at the same time more
and more constraints, stemming for example from environmental and
safety considerations, need to be satisfied. These increasing
demands can only be met when process nonlinearities and
constraints are explicitly considered in the controller design
stage. Nonlinear predictive control, the extension of well
established linear predictive control to the nonlinear world,
appears to be a well suited approach for this kind of problems. One of the main difficulties that often permits NMPC from being
applied in practice is the high online computational load: At each
sampling instance a nonlinear constrained finite horizon optimal
control problem needs to be solved numerically. In this paper we
discuss how recent system theoretic results for NMPC, namely the
use of the so-called quasi-infinite horizon approach to NMPC, can
improve its applicability by allowing a reduction of the
prediction horizon and thus the online computational load,
without affecting closed loop performance and stability. With the
use of a realistic process control example we demonstrate that
even fairly large scale problems can
be solved using NMPC techniques if state of the art optimization
techniques are combined with the quasi-infinite horizon technique.
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Date:
June 2000
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Type of Publication:
Internal Report 2000-8
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Publisher/Supervisor:
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File Download:
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Universität Stuttgart