Vehicle Lateral Motion Modeling Using Data-Driven Method

Chaehun Park, Cheolmin Jeong, Chang Mook Kang, Wonhee Kim, Young Seop Son

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Designing a control is the process of deriving the system's dynamic equation and finding a way to stabilize the derive system model. However, the derivation of the system model is complicated, and depending on the characteristics of the system, it may be difficult to design the controller. In this paper, we propose a data-driven model derivation method DMD (Dynamic Mode Decomposition) that simplifies the model derivation process and has similarities to the system. DMD can be used for complicated dynamic systems and has advantages in designing controllers because the derived model is linear. However, in order to take these advantages in the control design, it is first to show the similarity between the DMD-derived model and the system. Therefore, we used the vehicle dynamics simulation CarSim to derive various models and analyze how to derive validated models.

Original languageEnglish
Title of host publication5th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages767-770
Number of pages4
ISBN (Electronic)9781665456456
DOIs
StatePublished - 2023
Event5th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2023 - Virtual, Online, Indonesia
Duration: 20 Feb 202323 Feb 2023

Publication series

Name5th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2023

Conference

Conference5th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2023
Country/TerritoryIndonesia
CityVirtual, Online
Period20/02/2323/02/23

Keywords

  • Dynamic mode decomposition
  • Fast Fourier Transform
  • modeling
  • vehicle lateral dynamics

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