An Approach to Reduce Network Effects in an Industrial Control and Edge Computing Scenario

Rômulo V. de Omena, Danilo Santos, Angelo Perkusich

Abstract

The cloud-based nature of Industry 4.0 enhances its flexibility and scalability features. To support time-sensitive and mission-critical applications, whereby low latency and fast response are essential requirements, usually cloud computing resources should be placed closer to the industry. The Edge Computing concept combined with next-generation networks, such as 5G, may fulfill those requirements. This paper presents an experimental system setup that combines a Model Predictive Control approach with a compensation strategy to mitigate network delay and packet loss. The experimental system has two sides, namely, the edge and the local side. The former executes the controller and connects to the local side through a network. The latter is attached to the application and has lower computing capabilities. In our setup, the application under control is a two-wheeled mobile robot, which could act as an Automated Guided Vehicle. We defined two control objectives, the Point Stabilization, and the Trajectory Tracking, which ran through distinct network conditions, including delays and packet losses. These control objectives are only validation scenarios of the proposed approach but could be replaced by a real case path planner. The obtained results suggest that the approach is valid.

Download


Paper Citation


in Harvard Style

V. de Omena R., Santos D. and Perkusich A. (2021). An Approach to Reduce Network Effects in an Industrial Control and Edge Computing Scenario. In Proceedings of the 11th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER, ISBN 978-989-758-510-4, pages 296-303. DOI: 10.5220/0010496502960303


in Bibtex Style

@conference{closer21,
author={Rômulo V. de Omena and Danilo Santos and Angelo Perkusich},
title={An Approach to Reduce Network Effects in an Industrial Control and Edge Computing Scenario},
booktitle={Proceedings of the 11th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,},
year={2021},
pages={296-303},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010496502960303},
isbn={978-989-758-510-4},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Cloud Computing and Services Science - Volume 1: CLOSER,
TI - An Approach to Reduce Network Effects in an Industrial Control and Edge Computing Scenario
SN - 978-989-758-510-4
AU - V. de Omena R.
AU - Santos D.
AU - Perkusich A.
PY - 2021
SP - 296
EP - 303
DO - 10.5220/0010496502960303