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In relation to this article, we declare that there is no conflict of interest.
Publication history
Received October 2, 2003
Accepted December 16, 2003
articles This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Simulation-Based Learning of Cost-To-Go for Control of Nonlinear Processes

311 Ferst Dr. NW, School of Chemical and Biomolecular Engineering,, Georgia Institute of Technology, Atlanta, GA 30332-0100, USA
Korean Journal of Chemical Engineering, March 2004, 21(2), 338-344(7), 10.1007/BF02705417
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Abstract

In this paper, we present a simulation-based dynamic programming method that learns the ‘cost-to-go’ function in an iterative manner. The method is intended to combat two important drawbacks of the conventional Model Predictive Control (MPC) formulation, which are the potentially exorbitant online computational requirement and the inability to consider the future interplay between uncertainty and estimation in the optimal control calculation. We use a nonlinear Van de Vusse reactor to investigate the efficacy of the proposed approach and identify further research issues.

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