A Decomposition Algorithm for Mean-Variance Economic Model Predictive Control of Stochastic Linear Systems

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2014

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This paper presents a decomposition algorithm for solving the optimal control problem (OCP) that arises in Mean-Variance Economic Model Predictive Control of stochastic linear systems. The algorithm applies the alternating direction method of multipliers to a reformulation of the OCP that decomposes into small independent subproblems. We test the decomposition algorithm using a simple power management case study, in which the OCP is formulated as a convex quadratic program. Simulations show that the decomposition algorithm scales linearly in the number of uncertainty scenarios. Moreover, a parallel implementation of the algorithm is several orders of magnitude faster than state-of-the-art convex quadratic programming algorithms, provided that the number of uncertainty scenarios is large.
Original languageEnglish
Title of host publicationProceedings of the IEEE International Symposium on Intelligent Control (ISIC) 2014, Part of 2014 IEEE Multi-conference on Systems and Control
PublisherIEEE
Publication date2014
Pages1086-1093
ISBN (electronic)978-1-4799-7406-1
DOIs
StatePublished - 2014
Event2014 IEEE Multi-Conference on Systems and Control - Antibes, France

Conference

Conference2014 IEEE Multi-Conference on Systems and Control
LocationAntibes Congress Center
CountryFrance
CityAntibes
Period08/10/201410/10/2014
OtherAlso include the IEEE International Symposium on Intelligent Control (ISIC) 2014
Internet address
CitationsWeb of Science® Times Cited: No match on DOI
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