mdh.sePublications
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
Adaptive Runtime Response Time Control in PLC-based Real-Time Systems using Reinforcement Learning
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
RISE SICS, Sweden.ORCID iD: 0000-0002-1512-0844
RISE SICS, Sweden.ORCID iD: 0000-0003-1597-6738
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0001-5297-6548
Show others and affiliations
2018 (English)In: 13th International Symposium on Software Engineering for Adaptive and Self-Managing Systems SEAMS 18, 2018Conference paper, Published paper (Refereed)
Abstract [en]

Timing requirements such as constraints on response time are key characteristics of real-time systems and violations of these requirements might cause a total failure, particularly in hard real-time systems. Runtime monitoring of the system properties is of great importance to detect and mitigate such failures. Thus, a runtime control to preserve the system properties could improve the robustness of the system with respect to timing violations. Common control approaches may require a precise analytical model of the system which is difficult to be provided at design time. Reinforcement learning is a promising technique to provide adaptive model-free control when the environment is stochastic, and the control problem could be formulated as a Markov Decision Process. In this paper, we propose an adaptive runtime control using reinforcement learning for real-time programs based on Programmable Logic Controllers (PLCs), to meet the response time requirements. We demonstrate through multiple experiments that our approach could control the response time efficiently to satisfy the timing requirements.

Place, publisher, year, edition, pages
2018.
Keyword [en]
Adaptive response time control, PLC-based real-time programs, Runtime monitoring, Reinforcement learning
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-38955OAI: oai:DiVA.org:mdh-38955DiVA, id: diva2:1205964
Conference
13th International Symposium on Software Engineering for Adaptive and Self-Managing Systems SEAMS 18, 28 May 2018, Gothenburg, Sweden
Available from: 2018-05-15 Created: 2018-05-15 Last updated: 2018-05-15Bibliographically approved

Open Access in DiVA

No full text in DiVA

Authority records BETA

Helali Moghadam, MahshidSaadatmand, MehrdadLisper, Björn

Search in DiVA

By author/editor
Helali Moghadam, MahshidSaadatmand, MehrdadBohlin, MarkusLisper, Björn
By organisation
Embedded Systems
Computer Systems

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 27 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