loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Matthias Bahr ; Sebastian Reicherts ; Philipp Maximilian Sieberg ; Luca Morss and Dieter Schramm

Affiliation: Chair of Mechatronics, University of Duisburg-Essen, Lotharstraße 1, 47057 Duisburg and Germany

Keyword(s): Artificial Neural Network, Active Roll Control, Neural Controller, Reinforcement Learning, Actor-critic, Driving Maneuvers, Vehicle Dynamics, Machine Learning.

Related Ontology Subjects/Areas/Topics: Application Domains ; Automotive Industry ; Formal Methods ; Mobile Software and Services ; Neural Nets and Fuzzy Systems ; Simulation and Modeling ; Telecommunications ; Wireless Information Networks and Systems

Abstract: This paper deals with the application of machine learning for active roll control of motor vehicles. For this purpose, a special learning method based on reinforcement learning with an actor-critic model is used. It discusses and elaborates the basic design of the neural controller and its optimization for a fast and stable training. The methods mentioned are then validated. Both the training and the validation data are simulatively generated with the software environments MATLAB / Simulink and IPG CarMaker, while the architecture and training of the artificial neural network used is realized with the framework TensorFlow.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 44.211.117.101

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Bahr, M.; Reicherts, S.; Sieberg, P.; Morss, L. and Schramm, D. (2019). Development and Validation of Active Roll Control based on Actor-critic Neural Network Reinforcement Learning. In Proceedings of the 9th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH; ISBN 978-989-758-381-0; ISSN 2184-2841, SciTePress, pages 36-46. DOI: 10.5220/0007787400360046

@conference{simultech19,
author={Matthias Bahr. and Sebastian Reicherts. and Philipp Maximilian Sieberg. and Luca Morss. and Dieter Schramm.},
title={Development and Validation of Active Roll Control based on Actor-critic Neural Network Reinforcement Learning},
booktitle={Proceedings of the 9th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH},
year={2019},
pages={36-46},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007787400360046},
isbn={978-989-758-381-0},
issn={2184-2841},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - SIMULTECH
TI - Development and Validation of Active Roll Control based on Actor-critic Neural Network Reinforcement Learning
SN - 978-989-758-381-0
IS - 2184-2841
AU - Bahr, M.
AU - Reicherts, S.
AU - Sieberg, P.
AU - Morss, L.
AU - Schramm, D.
PY - 2019
SP - 36
EP - 46
DO - 10.5220/0007787400360046
PB - SciTePress