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http://dx.doi.org/10.22680/kasa2021.13.4.033

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

Cha, Hyunsoo (서울대학교 기계공학부)
Kim, Jayu (서울대학교 기계공학부)
Yi, Kyongsu (서울대학교 기계공학부)
Park, Jaeyong (현대자동차 연구개발본부)
Publication Information
Journal of Auto-vehicle Safety Association / v.13, no.4, 2021 , pp. 33-38 More about this Journal
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;
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  • Reference
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