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A Multi-Scale Model Predictive Control Strategy
Authors:
Arun
Krishnan¹ and Karlene A. Hoo*
¹
GSE
Systems, Maryland
*Department of Chemical
Engineering, Texas Tech University
ABSTRACT
Multi-scale systems defined on trees can provide local time and scale information about
the behavior of the process in contrast to the usual time-domain model and Fourier
transforms. Since the model predictive control (MPC) framework uses a model of the
process to determine the optimal control action, improving the model by using a
multi-scale approach will result in controller actions that can compensate for phenomena
that may occur at different scales. This work develops multi-scale models on trees,
describes how these time-scale models can be used in the MPC framework to represent both
the process and the disturbance, and proposes a new optimization strategy to determine the controller actions such that the optimal inputs, at the finer scales
reflect the inputs at the coarser scales. The performance of this multi-scale MPC
strategy is demonstrated on a continuous process and on a chemical batch
reactor.
Publication Information: Industrial & Engineering Chemistry Research,
Vol. 38, pp 1973-1986, 1999.
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