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首页> 外文会议>SAE World Congress Exhibition >Recursive Estimation of Vehicle Inertial Parameters Using Polynomial Chaos Theory via Vehicle Handling Model
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Recursive Estimation of Vehicle Inertial Parameters Using Polynomial Chaos Theory via Vehicle Handling Model

机译：递归估计多项式混沌理论通过车辆处理模型的媒体惯性参数

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摘要

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.
机译：提出了一种新的递归方法，用于使用众所周知的二维自由度（2dof）自行车车型来实时估计车辆的惯性参数。参数估计构建在多项式混沌理论的框架和最大似然估计上。然后可以基于牛顿方法的偏航速度的数值模拟来获得质量和偏航质量惯性矩的最可能值。为了提高估计精度，通过在估计过程中采用接受概率来修改牛顿方法。仿真研究结果表明，该方法可以提供快速收敛速度和准确的输出，与调整未识别参数的初始值的较小敏感度。

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