Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Stochastic Optimal Control of Dynamic Queue Systems: A Probabilistic Perspective
KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
Hong Kong Univ Sci & Technol, Elect & Comp Engn, Kowloon, Clear Water Bay, Hong Kong, Peoples R China..
KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0001-9940-5929
Hong Kong Univ Sci & Technol, Elect & Comp Engn, Kowloon, Clear Water Bay, Hong Kong, Peoples R China..
Show others and affiliations
2018 (English)In: 2018 15TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS AND VISION (ICARCV), IEEE , 2018, p. 837-842Conference paper, Published paper (Refereed)
Abstract [en]

Queue overflow of a dynamic queue system gives rise to the information loss (or packet loss) in the communication buffer or the decrease of throughput in the transportation network. This paper investigates a stochastic optimal control problem for dynamic queue systems when imposing probability constraints on queue overflows. We reformulate this problem as a Markov decision process (MDP) with safety constraints. We prove that both finite-horizon and infinite-horizon stochastic optimal control for MDP with such constraints can be transformed as a linear program (LP), respectively. Feasibility conditions are provided for the finite-horizon constrained control problem. Two implementation algorithms are designed under the assumption that only the state (not the state distribution) can be observed at each time instant. Simulation results compare optimal cost and state distribution among different scenarios, and show the probability constraint satisfaction by the proposed algorithms.

Place, publisher, year, edition, pages
IEEE , 2018. p. 837-842
Series
International Conference on Control Automation Robotics and Vision, ISSN 2474-2953
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-246317DOI: 10.1109/ICARCV.2018.8581152ISI: 000459847700141Scopus ID: 2-s2.0-85060814462ISBN: 978-1-5386-9582-1 (print)OAI: oai:DiVA.org:kth-246317DiVA, id: diva2:1297320
Conference
15th International Conference on Control, Automation, Robotics and Vision (ICARCV), NOV 18-21, 2018, Singapore, SINGAPORE
Note

QC 20190319

Available from: 2019-03-19 Created: 2019-03-19 Last updated: 2019-03-20Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Gao, YulongJohansson, Karl H.

Search in DiVA

By author/editor
Gao, YulongJohansson, Karl H.
By organisation
ACCESS Linnaeus Centre
Control Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 225 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf