Efficient Control Discretization Based on Turnpike Theory for Dynamic Optimization
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processes-05-00085-v3.pdf
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2.01 MB
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Checksum (MD5)
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Author(s)
Sahlodin, Ali Mohammad
Barton, Paul I
Date Issued
December 2017
Journal
Processes
Publisher
MDPI AG
Citation
Sahlodin, Ali, and Paul Barton. “Efficient Control Discretization Based on Turnpike Theory for Dynamic Optimization.” Processes, vol. 5, no. 4, Dec. 2017, p. 85.
Version
Final published version
Abstract
Dynamic optimization offers a great potential for maximizing performance of continuous processes from startup to shutdown by obtaining optimal trajectories for the control variables. However, numerical procedures for dynamic optimization can become prohibitively costly upon a sufficiently fine discretization of control trajectories, especially for large-scale dynamic process models. On the other hand, a coarse discretization of control trajectories is often incapable of representing the optimal solution, thereby leading to reduced performance. In this paper, a new control discretization approach for dynamic optimization of continuous processes is proposed. It builds upon turnpike theory in optimal control and exploits the solution structure for constructing the optimal trajectories and adaptively deciding the locations of the control discretization points. As a result, the proposed approach can potentially yield the same, or even improved, optimal solution with a coarser discretization than a conventional uniform discretization approach. It is shown via case studies that using the proposed approach can reduce the cost of dynamic optimization significantly, mainly due to introducing fewer optimization variables and cheaper sensitivity calculations during integration. Keywords: dynamic optimization; turnpike theory; control parametrization; adaptive discretization; optimal control
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Massachusetts Institute of Technology. Process Systems Engineering Laboratory
Terms of Use
Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.3390/pr5040085
