Identification of vehicle parameters using modified least square method in ADAMS/Car

M. B. Khaknejad, R. Kazemi, S. Azadi, A. Keshavarz
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引用次数: 9

Abstract

The chief purpose of this research is to identify the variable vehicle parameters considering the important role of these data in vehicle active safety systems. Generally, implementing sensors to measure these parameters could raise the cost of the product. Parameters of the reference sedan car we focus on are total mass of the car, yaw moment of inertia and distance between the vehicle centre of mass and its front axle as well as vehicle velocity using least square estimation with variable exponential forgetting factor. The estimator equations are derived based on the bicycle model of vehicle. The performance of designed estimator is evaluated by virtual simulations performed with the aim of the full vehicle model in ADAMS/Car in different maneuvers. The obtained results show the accurate, prompt and appropriate performance of the estimator in parameter estimation.
ADAMS/Car中基于改进最小二乘法的车辆参数辨识
本研究的主要目的是考虑到这些数据在车辆主动安全系统中的重要作用,识别可变车辆参数。一般来说,实现传感器来测量这些参数可能会提高产品的成本。采用带可变指数遗忘因子的最小二乘估计方法,对参考轿车的总质量、偏航惯性矩、车辆质心与前轴之间的距离以及车速进行了研究。基于车辆的自行车模型,推导了估计方程。以整车模型为目标,在ADAMS/Car软件中进行了不同机动情况下的虚拟仿真,对所设计的估计器进行了性能评价。实验结果表明,该估计器在参数估计中具有准确、迅速和适当的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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