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Adaptive Runtime Response Time Control in PLC-based Real-Time Systems using Reinforcement Learning
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
RISE SICS, Sweden.ORCID-id: 0000-0002-1512-0844
RISE SICS, Sweden.ORCID-id: 0000-0003-1597-6738
Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.ORCID-id: 0000-0001-5297-6548
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2018 (Engelska)Ingår i: ACM/IEEE 13th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2018, , co-located with International Conference on Software Engineering, ICSE 2018; Gothenburg; Sweden; 28 May 2018 through 29 May 2018; Code 138312, 2018, Vol. 28 May, s. 217-223Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
2018. Vol. 28 May, s. 217-223
Serie
Proceedings - International Conference on Software Engineering, ISSN 0270-5257
Nyckelord [en]
Adaptive response time control, PLC-based real-time programs, Runtime monitoring, Reinforcement learning
Nationell ämneskategori
Datorsystem
Identifikatorer
URN: urn:nbn:se:mdh:diva-38955DOI: 10.1145/3194133.3194153ISI: 000458799600029Scopus ID: 2-s2.0-85051555083OAI: oai:DiVA.org:mdh-38955DiVA, id: diva2:1205964
Konferens
13th International Symposium on Software Engineering for Adaptive and Self-Managing Systems SEAMS 18, 28 May 2018, Gothenburg, Sweden
Tillgänglig från: 2018-05-15 Skapad: 2018-05-15 Senast uppdaterad: 2019-03-07Bibliografiskt granskad

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Helali Moghadam, MahshidSaadatmand, MehrdadLisper, Björn

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