扩展卡尔曼滤波器
控制理论(社会学)
卡尔曼滤波器
滤波器(信号处理)
工程类
车辆动力学
不变扩展卡尔曼滤波器
推进
质心(相对论)
计算机科学
汽车工程
航空航天工程
机械
物理
控制(管理)
人工智能
电气工程
能量-动量关系
作者
Peter Lingman,Bengt Schmidtbauer
标识
DOI:10.1080/00423114.2002.11666217
摘要
SUMMARYKalman filtering is used as a powerful method to obtain accurate estimation of vehicle mass and road slope. First the problem of estimating the slope when the vehicle mass is known is studied using two different sensor configurations. One where speed is measured and one where both speed and specific-force is measured. A filter design principle is derived guaranteeing the estimation error under a worst case situation (when assuming first order dynamics). The simultaneous estimation problem required an Extended Kalman Filter (EKF) design when measuring speed only whereas the additional specific force ease yielded a simple filter structure with a time-variant measurement equation. Additionally the filter needs present propulsion force which in our case is calculated form the engine speed and amount of fuel injected. When the vehicle uses the foundation brakes the estimates are frozen since varying friction properties makes the braking force unknown. Both sensor configurations are concluded to be robust and accurate by simulation and experimental field trials.
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