A stochastic minimum principle and an adaptive pathwise algorithm for stochastic optimal control
We present a numerical method for finite-horizon stochastic optimal control models. We derive a stochastic minimum principle (SMP) and then develop a numerical method based on the direct solution of the SMP. The method combines Monte Carlo pathwise simulation and non-parametric interpolation methods...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Elsevier
2015
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| Online Access: | http://hdl.handle.net/1721.1/99483 |
| _version_ | 1811084354766503936 |
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| author | Parpas, Panos Webster, Mort |
| author2 | Massachusetts Institute of Technology. Engineering Systems Division |
| author_facet | Massachusetts Institute of Technology. Engineering Systems Division Parpas, Panos Webster, Mort |
| author_sort | Parpas, Panos |
| collection | MIT |
| description | We present a numerical method for finite-horizon stochastic optimal control models. We derive a stochastic minimum principle (SMP) and then develop a numerical method based on the direct solution of the SMP. The method combines Monte Carlo pathwise simulation and non-parametric interpolation methods. We present results from a standard linear quadratic control model, and a realistic case study that captures the stochastic dynamics of intermittent power generation in the context of optimal economic dispatch models. |
| first_indexed | 2024-09-23T12:49:05Z |
| format | Article |
| id | mit-1721.1/99483 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T12:49:05Z |
| publishDate | 2015 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | mit-1721.1/994832022-09-28T10:13:24Z A stochastic minimum principle and an adaptive pathwise algorithm for stochastic optimal control Parpas, Panos Webster, Mort Massachusetts Institute of Technology. Engineering Systems Division Webster, Mort We present a numerical method for finite-horizon stochastic optimal control models. We derive a stochastic minimum principle (SMP) and then develop a numerical method based on the direct solution of the SMP. The method combines Monte Carlo pathwise simulation and non-parametric interpolation methods. We present results from a standard linear quadratic control model, and a realistic case study that captures the stochastic dynamics of intermittent power generation in the context of optimal economic dispatch models. National Science Foundation (U.S.) (Grant 1128147) United States. Dept. of Energy. Office of Science (Biological and Environmental Research Program Grant DE-SC0005171) United States. Dept. of Energy. Office of Science (Biological and Environmental Research Program Grant DE-SC0003906) 2015-10-28T12:38:18Z 2015-10-28T12:38:18Z 2013-04 2012-12 Article http://purl.org/eprint/type/JournalArticle 00051098 http://hdl.handle.net/1721.1/99483 Parpas, Panos, and Mort Webster. “A Stochastic Minimum Principle and an Adaptive Pathwise Algorithm for Stochastic Optimal Control.” Automatica 49, no. 6 (June 2013): 1663–1671. en_US http://dx.doi.org/10.1016/j.automatica.2013.02.053 Automatica Creative Commons Attribution-Noncommercial-NoDerivatives http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier MIT Web Domain |
| spellingShingle | Parpas, Panos Webster, Mort A stochastic minimum principle and an adaptive pathwise algorithm for stochastic optimal control |
| title | A stochastic minimum principle and an adaptive pathwise algorithm for stochastic optimal control |
| title_full | A stochastic minimum principle and an adaptive pathwise algorithm for stochastic optimal control |
| title_fullStr | A stochastic minimum principle and an adaptive pathwise algorithm for stochastic optimal control |
| title_full_unstemmed | A stochastic minimum principle and an adaptive pathwise algorithm for stochastic optimal control |
| title_short | A stochastic minimum principle and an adaptive pathwise algorithm for stochastic optimal control |
| title_sort | stochastic minimum principle and an adaptive pathwise algorithm for stochastic optimal control |
| url | http://hdl.handle.net/1721.1/99483 |
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