Recursive Estimation of Vehicle Inertial Parameters Using Polynomial Chaos Theory via Vehicle Handling Model

Zeyu Ma, James Yang, Ming Jiang, Yunqing Zhang

Research output: Contribution to journalConference article

Abstract

A new recursive method is presented for real-time estimating the inertia parameters of a vehicle using the well-known Two-Degree-of- Freedom (2DOF) bicycle car model. The parameter estimation is built on the framework of polynomial chaos theory and maximum likelihood estimation. Then the most likely value of both the mass and yaw mass moment of inertia can be obtained based on the numerical simulations of yaw velocity by Newton method. To improve the estimation accuracy, the Newton method is modified by employing the acceptance probability to escape from the local minima during the estimation process. The results of the simulation study suggest that the proposed method can provide quick convergence speed and accurate outputs together with less sensitivity to tuning the initial values of the unidentified parameters.

Original languageEnglish
JournalSAE Technical Papers
Volume2015-April
Issue numberApril
DOIs
StatePublished - Apr 14 2015
EventSAE 2015 World Congress and Exhibition - Detroit, United States
Duration: Apr 21 2015Apr 23 2015

Keywords

  • 2DOF bicycle car model
  • Parameter estimation method
  • Polynomial chaos expansion

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