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Adaptive Runtime Response Time Control in PLC-based Real-Time Systems using Reinforcement Learning

Publication Type:

Conference/Workshop Paper

Venue:

13th International Symposium on Software Engineering for Adaptive and Self-Managing Systems


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 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.

Bibtex

@inproceedings{Helali Moghadam5053,
author = {Mahshid Helali Moghadam and Mehrdad Saadatmand and Markus Borg and Markus Bohlin and Bj{\"o}rn Lisper},
title = {Adaptive Runtime Response Time Control in PLC-based Real-Time Systems using Reinforcement Learning},
month = {May},
year = {2018},
booktitle = {13th International Symposium on Software Engineering for Adaptive and Self-Managing Systems},
url = {http://www.es.mdh.se/publications/5053-}
}