Edge-Enabled Explainable Reinforcement Learning for Safe and Scalable Feedback Control Loop Optimization in IoT-Integrated Industrial Automation Systems

V. Subba Ramaiah, Kota Lakshmi Prasanna, Kasula Raghu, Padma Parshapu, J. Vinisha, Syed Zahidur Rashid

2025

Abstract

The introduction of artificial intelligence in industrial automation has led to the control systems reaching a much higher level, however, it seems that the traditional approaches are often not realtime deployable, scalable and explainable. This study presents an edge-enabled explainable RL framework to optimize feedback control loops in IoT-integrated industrial systems. Unlike conventional RL models performing simulations, we demonstrate the proposed system in real environments by an edge device-based resource economical deep reinforcement learning. The framework guarantees safety-sensitive decision making, interpretable control, and portability to a variety of heterogeneous industrial missions. This work provides powerful combined solutions by integrating lightweight AI models with on-the-fly IoT data streams for adaptable, energy efficient, and automated control operations. Moreover, automatic hyperparameter tunning and multi-agent scalability are introduced to improve the robustness and the real-time performance in such complex industrial environment. The framework overcomes limitations of existing models and defines a transferable and modular approach for Industry 4.0 ready automation systems.

Paper Citation

in Harvard Style

Ramaiah V., Prasanna K., Raghu K., Parshapu P., Vinisha J. and Rashid S. (2025). Edge-Enabled Explainable Reinforcement Learning for Safe and Scalable Feedback Control Loop Optimization in IoT-Integrated Industrial Automation Systems. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 593-599. DOI: 10.5220/0013886900004919

in Bibtex Style

@conference{icrdicct`2525,
author={V. Ramaiah and Kota Prasanna and Kasula Raghu and Padma Parshapu and J. Vinisha and Syed Rashid},
title={Edge-Enabled Explainable Reinforcement Learning for Safe and Scalable Feedback Control Loop Optimization in IoT-Integrated Industrial Automation Systems},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={593-599},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013886900004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Edge-Enabled Explainable Reinforcement Learning for Safe and Scalable Feedback Control Loop Optimization in IoT-Integrated Industrial Automation Systems
SN - 978-989-758-777-1
AU - Ramaiah V.
AU - Prasanna K.
AU - Raghu K.
AU - Parshapu P.
AU - Vinisha J.
AU - Rashid S.
PY - 2025
SP - 593
EP - 599
DO - 10.5220/0013886900004919
PB - SciTePress