中文版 | English
Title

Data-Driven Probabilistic Model of Magneto-Rheological Damper for Intelligent Vehicles using Gaussian Processes

Author
Corresponding AuthorCui,Yunduan
DOI
Publication Years
2022
ISBN
978-1-6654-6881-7
Source Title
Volume
2022-October
Pages
1094-1099
Conference Date
8-12 Oct. 2022
Conference Place
Macau, China
Abstract
In this paper, we focus on Magneto-rheological damper (MRD) which is essential to intelligent vehicle suspension systems. We attempt to build a data-driven model of MRD with complex dynamics in a probabilistic view in order to address the issues of sample efficiency and the robustness against environmental disturbances in the current non-parametric approaches. Compared with the conventional approaches like neural networks that are sensitive to the noisy data and require a massively training samples, we employ Gaussian processes to model the target system in a full Bayesian way while considering both mean and variance of the prediction. Evaluated by a MRD simulation platform in different working conditions and a noisy external environment, the model learned via GP clearly demonstrated not only good performances in prediction accuracy and uncertainties representation, but also a good balance between the model quality and computational complexity under different sparse scales. These results indicate the great potential of GP as an emerging direction of modeling MRD in intelligent vehicles.
Keywords
SUSTech Authorship
Others
Language
English
URL[Source Record]
Indexed By
EI Accession Number
20224613130778
EI Keywords
Automobile suspensions ; Bayesian networks ; Complex networks ; Gaussian noise (electronic) ; Simulation platform
ESI Classification Code
Automobile and Smaller Vehicle Components:662.4 ; Computer Systems and Equipment:722 ; Computer Applications:723.5 ; Combinatorial Mathematics, Includes Graph Theory, Set Theory:921.4 ; Probability Theory:922.1 ; Mathematical Statistics:922.2
Scopus EID
2-s2.0-85141865459
Data Source
Scopus
PDF urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9922006
Citation statistics
Cited Times [WOS]:0
Document TypeConference paper
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/411861
DepartmentCollege of Engineering
Affiliation
1.Shenzhen Institute of Advanced Technology (SIAT),Chinese Academy of Sciences,China
2.Siat Branch,Shenzhen Institute of Artificial Intelligence and Robotics for Society,Shenzhen,China
3.College of Engineering,Southern University of Science and Technology,China
First Author AffilicationCollege of Engineering
Recommended Citation
GB/T 7714
Wang,Jincheng,Xu,Kun,Shao,Cuiping,et al. Data-Driven Probabilistic Model of Magneto-Rheological Damper for Intelligent Vehicles using Gaussian Processes[C],2022:1094-1099.
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