Using Imitation Learning to Implement Control Orchestration for Smart Chassis

Sarah Khelil, Bruno Moonsuez, Maud Geoffriault, Anh Do

2024

Abstract

Despite the advances in control allocation for over-actuated systems, the need for a comprehensive, optimized, and safe solution remains ongoing. Traditional methods, though mature, struggle with the complexities of coupled non-linear allocation and the need for extensive computational resources. Machine learning may provide significant advantages through its generalization and adaptation capabilities, especially in scenarios where linear approximations are employed to reduce computational burdens or when the effectiveness of actuators is uncertain. Recent advances in imitation learning, particularly behavioral cloning, and deep reinforcement learning have demonstrated promising results in addressing these challenges. This paper aims to determine the potential of using machine learning in control orchestration for smart chassis to go beyond allocation issues to include interaction management across systems, resource balance, and safety and performance limits. We present a set of techniques that we believe are relevant to experiment to address potential challenges like prediction and complexity for control allocation in smart chassis systems, which will be tested in the upcoming articles.

Paper Citation

in Harvard Style

Khelil S., Moonsuez B., Geoffriault M. and Do A. (2024). Using Imitation Learning to Implement Control Orchestration for Smart Chassis. In Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO; ISBN 978-989-758-717-7, SciTePress, pages 462-471. DOI: 10.5220/0013049300003822

in Bibtex Style

@conference{icinco24,
author={Sarah Khelil and Bruno Moonsuez and Maud Geoffriault and Anh Do},
title={Using Imitation Learning to Implement Control Orchestration for Smart Chassis},
booktitle={Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO},
year={2024},
pages={462-471},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013049300003822},
isbn={978-989-758-717-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 21st International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO
TI - Using Imitation Learning to Implement Control Orchestration for Smart Chassis
SN - 978-989-758-717-7
AU - Khelil S.
AU - Moonsuez B.
AU - Geoffriault M.
AU - Do A.
PY - 2024
SP - 462
EP - 471
DO - 10.5220/0013049300003822
PB - SciTePress