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Vehicle Running State Estimation by Adaptive Soft-Sensing Algorithm

机译：自适应软传感算法的车辆行驶状态估计

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摘要

Vehicle running state adaptive unscented Kalman filter soft-sensing algorithm is put forward in this paper based on traditional UKF which can estimate vehicle running state parameters and suboptimal Sage-Husa noise estimator which can effectively solve the problem of noises varying with time. Meanwhile 3-DOF dynamic model of vehicle and HSRI tire model are established. So vehicle running state can be accurately estimated by fusing the low-cost measurement information of longitudinal and lateral acceleration and hand wheel steering angle. Under the typical working condition, AUKF soft-sensing algorithm is verified with substantial vehicle tests. Comparing with UKF soft-sensing algorithm, the result indicates AUKF soft-sensing algorithm has a good performance in robustness and is able to realize the effective estimation of vehicle running state more precisely than UKF soft-sensing algorithm.
机译：提出了一种基于传统UKF的车辆行驶状态自适应无味卡尔曼滤波器软检测算法，该算法可以估算车辆行驶状态参数，并采用次优Sage-Husa噪声估计器，可以有效解决噪声随时间变化的问题。同时建立了汽车三自由度动力学模型和HSRI轮胎模型。因此，通过融合低成本的纵向和横向加速度以及方向盘转向角的测量信息，可以准确地估计车辆的行驶状态。在典型的工作条件下，AUKF软传感算法已通过大量的车辆测试验证。与UKF软传感算法相比，结果表明AUKF软传感算法在鲁棒性方面具有良好的性能，比UKF软传感算法能够更精确地实现对车辆行驶状态的有效估计。

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  • 来源
    《Mathematical Problems in Engineering》 |2018年第15期|3106329.1-3106329.9|共9页
  • 作者

    Hao Liang; Guo Lixin; Liu Shuwei;

  • 作者单位

    Liaoning Univ Technol Automobile & Traff Engn Coll Jinzhou 121000 Liaoning Peoples R China|Northeastern Univ Automot Engn Sch Mech Engn & Automat Shenyang 110819 Liaoning Peoples R China;

    Northeastern Univ Automot Engn Sch Mech Engn & Automat Shenyang 110819 Liaoning Peoples R China;

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