Real-Time Identification of Vehicle Motion-Modes

Chen, T; Zheng, M; Zhang, N; Luo, L; Pan, Y

Real-Time Identification of Vehicle Motion-Modes

Chen, T Zheng, M Zhang, N

Luo, L Pan, Y

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Publisher:: Springer Nature
Publication Type:: Conference Proceeding
Citation:: Vibration Engineering for a Sustainable Future, 2021, 2, pp. 167-173
Issue Date:: 2021-04-22

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Field	Value	Language
dc.contributor.author	Chen, T
dc.contributor.author	Zheng, M
dc.contributor.author	Zhang, N https://orcid.org/0000-0001-6408-0044
dc.contributor.author	Luo, L
dc.contributor.author	Pan, Y
dc.date	2019-11-18
dc.date.accessioned	2022-05-15T04:23:05Z
dc.date.available	2022-05-15T04:23:05Z
dc.date.issued	2021-04-22
dc.identifier.citation	Vibration Engineering for a Sustainable Future, 2021, 2, pp. 167-173
dc.identifier.isbn	3030481530
dc.identifier.isbn	9783030481537
dc.identifier.uri	http://hdl.handle.net/10453/157362
dc.description.abstract	A linear 7-DOF vehicle model includes seven motion-modes. Recognition of these motion-modes can improve chassis dynamic control strategies for active suspensions. The motion-mode energy method (MEM) was proposed to identify dominant vehicle motion-mode at each time instant. This method is based on an assumption that all 14 vehicle states and four road inputs are given. It is hard to achieve in practice. This paper proposes a novel method to classify vehicle dominant motion-modes in real-time based on long short-term memory (LSTM) classification networks. The effectiveness and accuracy of the trained networks are verified on a 7-DOF vehicle model under several driving scenarios. This method helps design of vehicle dynamic control strategies and improves active suspension performance.
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	Vibration Engineering for a Sustainable Future
dc.relation.ispartof	Asia-Pacific Vibration Conference
dc.relation.isbasedon	10.1007/978-3-030-48153-7_22
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Real-Time Identification of Vehicle Motion-Modes
dc.type	Conference Proceeding
utslib.citation.volume	2
utslib.location.activity	Sydney, Australia
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-05-15T04:23:03Z
pubs.finish-date	2019-11-20
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
pubs.start-date	2019-11-18
pubs.volume	2
dc.location	Switzerland

Abstract:

A linear 7-DOF vehicle model includes seven motion-modes. Recognition of these motion-modes can improve chassis dynamic control strategies for active suspensions. The motion-mode energy method (MEM) was proposed to identify dominant vehicle motion-mode at each time instant. This method is based on an assumption that all 14 vehicle states and four road inputs are given. It is hard to achieve in practice. This paper proposes a novel method to classify vehicle dominant motion-modes in real-time based on long short-term memory (LSTM) classification networks. The effectiveness and accuracy of the trained networks are verified on a 7-DOF vehicle model under several driving scenarios. This method helps design of vehicle dynamic control strategies and improves active suspension performance.

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http://hdl.handle.net/10453/157362