### Abstract

Original language | English |
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Title of host publication | Proceedings of the IEEE International Symposium on Intelligent Control (ISIC) 2014, Part of 2014 IEEE Multi-conference on Systems and Control |

Publisher | IEEE |

Publication date | 2014 |

Pages | 1086-1093 |

ISBN (Electronic) | 978-1-4799-7406-1 |

DOIs | |

Publication status | Published - 2014 |

Event | 2014 IEEE Multi-Conference on Systems and Control - Antibes Congress Center, Antibes, France Duration: 8 Oct 2014 → 10 Oct 2014 http://www.msc2014.org/ |

### Conference

Conference | 2014 IEEE Multi-Conference on Systems and Control |
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Location | Antibes Congress Center |

Country | France |

City | Antibes |

Period | 08/10/2014 → 10/10/2014 |

Other | Also include the IEEE International Symposium on Intelligent Control (ISIC) 2014 |

Internet address |

### Cite this

*Proceedings of the IEEE International Symposium on Intelligent Control (ISIC) 2014, Part of 2014 IEEE Multi-conference on Systems and Control*(pp. 1086-1093). IEEE. https://doi.org/10.1109/ISIC.2014.6967612

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*Proceedings of the IEEE International Symposium on Intelligent Control (ISIC) 2014, Part of 2014 IEEE Multi-conference on Systems and Control.*IEEE, pp. 1086-1093, 2014 IEEE Multi-Conference on Systems and Control, Antibes, France, 08/10/2014. https://doi.org/10.1109/ISIC.2014.6967612

**A Decomposition Algorithm for Mean-Variance Economic Model Predictive Control of Stochastic Linear Systems.** / Sokoler, Leo Emil; Dammann, Bernd; Madsen, Henrik; Jørgensen, John Bagterp.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

TY - GEN

T1 - A Decomposition Algorithm for Mean-Variance Economic Model Predictive Control of Stochastic Linear Systems

AU - Sokoler, Leo Emil

AU - Dammann, Bernd

AU - Madsen, Henrik

AU - Jørgensen, John Bagterp

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

U2 - 10.1109/ISIC.2014.6967612

DO - 10.1109/ISIC.2014.6967612

M3 - Article in proceedings

SP - 1086

EP - 1093

BT - Proceedings of the IEEE International Symposium on Intelligent Control (ISIC) 2014, Part of 2014 IEEE Multi-conference on Systems and Control

PB - IEEE

ER -