Data-Driven Probabilistic Model of Magneto-Rheological Damper for Intelligent Vehicles using Gaussian Processes
8-12 Oct. 2022
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.
|EI Accession Number|
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
Cited Times [WOS]:0
|Document Type||Conference paper|
|Department||College of Engineering|
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 Affilication||College of Engineering|
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.
|Files in This Item:||There are no files associated with this item.|
|Recommend this item|
|Export to Endnote|
|Export to Excel|
|Export to Csv|
|Similar articles in Google Scholar|
|Similar articles in Baidu Scholar|
|Similar articles in Bing Scholar|
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.