基于自适应状态转移模型的车辆状态估计

Feihua Huang, Yan Gao, Chunyun Fu, A. Gostar, R. Hoseinnezhad, Minghui Hu
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引用次数: 1

摘要

车辆底盘控制系统的性能取决于控制系统输入信息的准确性。对于底盘控制所必需的一些重要的车辆状态,如车辆的纵向和横向速度,无法以低成本直接测量。在现有文献中,许多车辆状态估计方案都是基于车辆动态模型设计的。这些模型不可避免地涉及到不易测量或估计的轮胎力的获取。本文提出了一种基于直观的车辆运动学模型的状态估计器,该模型不依赖于任何胎力信息。所提出的状态估计器具有较低的复杂度和计算量。此外,为了保证有竞争力的估计性能,该估计器中使用的状态转移模型被设计为自适应车载传感器的测量。在仿真研究中,所提估计器在不同仿真条件下均能提供准确的估计结果,验证了所提车辆状态估计器的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Vehicle State Estimation Based on Adaptive State Transition Model
The performance of vehicle chassis control systems relies on the accuracy of input information to the control systems. Some important vehicle states which are necessary for chassis control cannot be directly measured at low cost, such as the vehicle longitudinal and lateral velocities. In the existing literature, many vehicle state estimation solutions are designed based on vehicle dynamic models. These models inevitably involve the acquisition of tire forces which cannot be easily measured or estimated. In this paper, a vehicle state estimator is proposed based on a straightforward vehicle kinematic model, which does not rely on any tire force information. The complexity and computation load of the proposed state estimator is low. Besides, to ensure competitive estimation performance, the state transition model used in this estimator is designed to be adaptive to the on-board sensor measurements. In the simulation studies, the proposed estimator is able to provide accurate estimation results under different simulation conditions, which verifies the effectiveness of the proposed vehicle state estimator.
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