Abstract Even though driveshaft torsion is a fundamental variable in vehicle dynamics, it is difficult to be measured or estimated due to the need for high precision encoders or because of integration estimation errors. Furthermore, gear shifting in the driveline affects driveshaft torsion estimation, as it abruptly changes powertrain dynamics. Another issue in driveshaft torsion estimation is the influence of the road slope, which disturbs the system, and may or may not be measured. This paper proposes the estimation of driveshaft torsion by a robust filter for discrete-time Markov jump linear systems, with or without road slope information. This algorithm is tested for a truck bodywork. Results show that the estimation delivers online results as accurate as offline estimation methods, especially when the road slope is known. The proposed filter is capable of estimating the torsion even in scenarios of high plant uncertainty, where an LMI-based filter cannot find a feasible solution.

中文翻译：

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用于重型车辆传动轴扭转估计的马尔可夫滤波

摘要 尽管传动轴扭矩是车辆动力学中的一个基本变量，但由于需要高精度编码器或积分估计误差，因此难以测量或估计。此外，传动系统中的换档会影响传动轴扭矩估计，因为它会突然改变动力系统动态。传动轴扭转估计中的另一个问题是道路坡度的影响，它会干扰系统，可能会也可能不会被测量。本文提出了通过用于离散时间马尔可夫跳跃线性系统的鲁棒滤波器来估计传动轴扭矩的方法，无论是否有道路坡度信息。该算法针对卡车车身进行了测试。结果表明，估计提供的在线结果与离线估计方法一样准确，尤其是在道路坡度已知的情况下。