A Comparative Study between the Parameter-Optimized Pacejka Model and Artificial Neural Network Model for Tire Force Estimation

Hyunsoo Cha; Jayu Kim; Kyongsu Yi; Jaeyong Park

doi:10.22680/kasa2021.13.4.033

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2021 Vol.13, Issue 4 Preview Page Next Page

A Comparative Study between the Parameter-Optimized Pacejka Model and Artificial Neural Network Model for Tire Force Estimation 타이어 힘 추정을 위한 파라미터 최적화 파제카 모델과 인공 신경망 모델 간의 비교 연구

31 December 2021. pp. 33-38

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Abstract

This paper presents a comparative study between the parameter-optimized Pacejka model and artificial neural network model for the tire force estimation. The two different approaches are investigated and compared in this study. First, offline optimization is conducted based on Pacejka Magic Formula model to determine the proper parameter set for the minimization of tire force error between the model and test data set. Second, deep neural network model is used to fit the model to the tire test data set. The actual tire forces are measured using MTS Flat-Track test platform and the measurements are used as the reference tire data set. The focus of this study is on the applicability of machine learning technique to tire force estimation. It is shown via the regression results that the deep neural network model is more effective in describing the tire force than the parameter-optimized Pacejka model.

Keywords

Tire force estimation

Tire model

Artificial neural network

키워드

타이어 힘 추정

타이어 모델

인공 신경망

References

Pacejka, H., 2005, Tire and vehicle dynamics. Elsevier.

Pacejka, H. B. and Bakker, E., 1992, The magic formula tyre model. Vehicle system dynamics, 21(S1), 1~18. 10.1080/00423119208969994

Ortiz, A. J. A. C., Cabrera, J. A., Guerra, A. J. and Simon, A., 2006, An easy procedure to determine Magic Formula parameters: a comparative study between the starting value optimization technique and the IMMa optimization algorithm, Vehicle System Dynamics, 44(9), 689~718. 10.1080/00423110600574558

Xu, N., Askari, H., Huang, Y., Zhou, J. and Khajepour, A., 2020, Tire Force Estimation in Intelligent Tires Using Machine Learning, IEEE Transactions on Intelligent Transportation Systems. 10.1109/TITS.2020.3038155

Kim, P., 2017, Matlab deep learning, With machine learning, neural networks and artificial intelligence, 130, 21. 10.1007/978-1-4842-2845-6_1

Abdi, H., 1994, A neural network primer, Journal of Biological Systems, 2(03), 247~281. 10.1142/S0218339094000179

Boyd, S., Boyd, S. P. and Vandenberghe, L., 2004, Convex optimization, Cambridge university press. 10.1017/CBO9780511804441

Information

Publisher :Korean Auto-vehicle Safety Association
Publisher(Ko) :한국자동차모빌리티안전학회
Journal Title :Journal of Auto-vehicle Safety Association
Journal Title(Ko) :자동차안전학회지
Volume : 13
No :4
Pages :33-38
Received Date : 2021-05-03
Accepted Date : 2021-08-31
DOI :https://doi.org/10.22680/kasa2021.13.4.033

Journal of Auto-vehicle Safety Association ISSN:2005-9396(Print) 자동차안전학회지

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