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首页> 外文期刊>Proceedings of the Institution of Mechanical Engineers. Part K, Journal of Multi-body Dynamics >The identifying extended Kalman filter: parametric system identification of a vehicle handling model
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The identifying extended Kalman filter: parametric system identification of a vehicle handling model

机译：识别扩展卡尔曼滤波器：车辆操纵模型的参数系统识别

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摘要

This article considers a novel method for estimating parameters in a vehicle-handling dynamic model using a recursive filter. The well-known extended Kalman filter - which is widely used for real-time state estimation of vehicle dynamics - is used here in an unorthodox fashion; a model is prescribed for the sensors alone, and the state vector is replaced by a set of unknown model parameters. With the aid of two simple tuning parameters, the system self-regulates its estimates of parameter and sensor errors, and hence smoothly identifies optimal parameter choices. The method makes one contentious assumption that vehicle lateral velocity (or body sideslip angle) is available as a measurement, along with the more conventionally available yaw velocity state. However, the article demonstrates that by using the new generation of combined GPS/inertial body motion measurement systems, a suitable lateral velocity signal is indeed measurable. The system identification is thus demonstrated in simulation, and also proved by successful parametrization of a model, using test vehicle data. The identifying extended Kalman filter has applications in model validation - for example, acting as a reference between vehicle behaviour and higher-order multi-body models - and it could also be operated in a real-time capacity to adapt parameters in model-based vehicle control applications.
机译：本文考虑了一种使用递归滤波器来估计车辆操纵动态模型中参数的新颖方法。众所周知，扩展的卡尔曼滤波器-被广泛用于车辆动力学的实时状态估计-在这里以一种非正统的方式使用。仅为传感器规定了一个模型，然后用一组未知的模型参数代替状态向量。借助于两个简单的调整参数，系统可以自我调节对参数和传感器误差的估计，从而平稳地确定最佳参数选择。该方法提出了一个有争议的假设，即可以将车辆横向速度（或车身侧滑角）以及更常规的可用偏航速度状态作为度量。但是，该文章证明，通过使用新一代的组合式GPS /惯性运动测量系统，确实可以测量合适的横向速度信号。因此，系统识别在仿真中得到了证明，并且还通过使用测试车辆数据对模型进行了成功的参数化来证明。识别出的扩展卡尔曼滤波器在模型验证中具有应用-例如，充当车辆行为和高阶多体模型之间的参考-并且还可以实时运行以适应基于模型的车辆中的参数控制应用程序。

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