A Performance Benchmark of Formulated Methods for Forecast or Reconstruct Trajectories Associated to the Process Control in Industry 4.0

Davi Neves, Ricardo Augusto Rabelo Oliveira

2022

Abstract

Manufacturing processes are generally modeled through dynamic systems, whose solutions establish a tool for control theory, essential in the elaboration of industrial automation, a pillar of the fourth revolution. Understanding and mastering these technological procedures correspond to the ability to determine and analyze the solutions of a system of differential equations, in order to deploy smart devices in a production line, such as the robotic arm, because this trajectories can be always associated with the running of any equipment. Currently there are many formulated methods to determine (or forecast) these curves, through numerical or stochastic tools, the focus in this work are those capable of reconstructing a state space, such as the Koopman’s operator, convolutional neural network and reinforcement learning technique. Therefore, based on the solutions provided by these methods, a benchmark will assembled to compare them, using topological measures such as Shannon entropy, Lyapunov exponent and Hurst coefficient, thus defining the effectiveness of each one.

Paper Citation

in Harvard Style

Neves D. and Oliveira R. (2022). A Performance Benchmark of Formulated Methods for Forecast or Reconstruct Trajectories Associated to the Process Control in Industry 4.0. In Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-569-2, pages 594-601. DOI: 10.5220/0011086800003179

in Bibtex Style

@conference{iceis22,
author={Davi Neves and Ricardo Oliveira},
title={A Performance Benchmark of Formulated Methods for Forecast or Reconstruct Trajectories Associated to the Process Control in Industry 4.0},
booktitle={Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2022},
pages={594-601},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011086800003179},
isbn={978-989-758-569-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - A Performance Benchmark of Formulated Methods for Forecast or Reconstruct Trajectories Associated to the Process Control in Industry 4.0
SN - 978-989-758-569-2
AU - Neves D.
AU - Oliveira R.
PY - 2022
SP - 594
EP - 601
DO - 10.5220/0011086800003179