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

Wang，Jincheng1,3; Xu，Kun1,2; Shao，Cuiping1; Peng，Lei1; Cui，Yunduan1,2

10.1109/ITSC55140.2022.9922006

2022

978-1-6654-6881-7

IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC

2022-October

1094-1099

8-12 Oct. 2022

Macau, China

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.

Uncertainty Intelligent vehicles Computational modeling Magnetomechanical effects Gaussian processes Predictive models Probabilistic logic

Others

English

EI

20224613130778

Automobile suspensions ; Bayesian networks ; Complex networks ; Gaussian noise (electronic) ; Simulation platform

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

2-s2.0-85141865459

Scopus

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

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.