A distributed optimization algorithm for model predictive control problem of linear systems under communication noise

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Abstract

We study the distributed model predictive control (DMPC) problem for a group of linear discrete-time systems with both local constraints and global constraints in the presence of stochastic communication noise. The dual form of the DMPC optimization problem is transformed into a stochastic distributed consensus optimization problem by modeling the exchanged variables as stochastic ones and a novel stochastic alternating direction multiplier method (ADMM) is proposed to solve it in a fully distributed way. The effectiveness of the proposed stochastic ADMM algorithm is verified through an simulation example.

Original languageEnglish
Title of host publicationProceedings of the 38th Chinese Control Conference, CCC 2019
EditorsMinyue Fu, Jian Sun
PublisherIEEE Computer Society
Pages2959-2964
Number of pages6
ISBN (Electronic)9789881563972
DOIs
StatePublished - Jul 2019
Event38th Chinese Control Conference, CCC 2019 - Guangzhou, China
Duration: 27 Jul 201930 Jul 2019

Publication series

NameChinese Control Conference, CCC
Volume2019-July
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927

Conference

Conference38th Chinese Control Conference, CCC 2019
Country/TerritoryChina
CityGuangzhou
Period27/07/1930/07/19

Keywords

  • Communication noise
  • Coupled constraints
  • Distributed model predictive control (DMPC)
  • Stochastic alternating direction multiplier method (ADMM)

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