Paper Info
Title
Adaptive runtime response time control in PLC-based real-time systems using reinforcement learning.
Abstract
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 check the system status 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.
Year
DOI
Venue
2018
10.1145/3194133.3194153
SEAMS@ICSE
Keywords
Field
DocType
Adaptive response time control, PLC-based real-time programs, Runtime monitoring, Reinforcement learning
Adaptive system,Computer science,Response time,Markov decision process,Robustness (computer science),Control engineering,Programmable logic controller,Reinforcement learning
Conference
ISSN
ISBN
Citations 
2157-2305
978-1-4503-5715-9
0
PageRank 
References 
Authors
0.34
10
5
Name
Order
Citations
PageRank
Mahshid Helali Moghadam164.25
Mehrdad Saadatmand24313.11
Markus Borg326029.79
Markus Bohlin47714.24
Björn Lisper557045.29