Journal of Auto-vehicle Safety Association (자동차안전학회지)

Korean Auto-vehicle Safety Association (한국자동차안전학회)
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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

  • 차현수 (서울대학교 기계공학부) ;
  • 김자유 (서울대학교 기계공학부) ;
  • 이경수 (서울대학교 기계공학부) ;
  • 박재용 (현대자동차 연구개발본부)
  • Received : 2021.05.03
  • Accepted : 2021.08.31
  • Published : 2021.12.31
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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

Acknowledgement

본 연구는 미래창조과학부 재원으로 한국연구재단(NRF-2016R1E1A1A01943543)의 지원을 받아 수행된 연구임.

References

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