Steer-by-Wire systems replace mechanical linkages, which provide benefits like weight reduction, design flexibility, and compatibility with autonomous driving. However, they are susceptible to high-frequency disturbances from unintentional driver torque, known as driver impedance, which can degrade steering performance. Existing approaches either rely on direct torque sensors, which are costly and impractical, or lack the temporal resolution to capture rapid, high-frequency driver-induced disturbances. We address this limitation by designing a Kalman filter-based disturbance observer that estimates high-frequency driver torque using only motor state measurements. We model the drivers passive torque as an extended state using a PT1-lag approximation and integrate it into both linear and nonlinear Steer-by-Wire system models. In this paper, we present the design, implementation and simulation of this disturbance observer with an evaluation of different Kalman filter variants. Our findings indicate that the proposed disturbance observer accurately reconstructs driver-induced disturbances with only minimal delay 14ms. We show that a nonlinear extended Kalman Filter outperforms its linear counterpart in handling frictional nonlinearities, improving estimation during transitions from static to dynamic friction. Given the study's methodology, it was unavoidable to rely on simulation-based validation rather than real-world experimentation. Further studies are needed to investigate the robustness of the observers under real-world driving conditions.

翻译：线控转向系统取代了机械连杆机构，具有减轻重量、设计灵活以及与自动驾驶兼容等优势。然而，该系统易受来自驾驶员无意识扭矩的高频扰动（称为驾驶员阻抗）影响，这可能导致转向性能下降。现有方法要么依赖直接扭矩传感器（成本高昂且不实用），要么缺乏捕捉快速、高频驾驶员诱发扰动所需的时间分辨率。我们通过设计一种基于卡尔曼滤波的扰动观测器来解决这一局限，该观测器仅利用电机状态测量值即可估计高频驾驶员扭矩。我们采用PT1滞后近似将驾驶员的被动扭矩建模为扩展状态，并将其集成到线性和非线性线控转向系统模型中。本文介绍了该扰动观测器的设计、实现与仿真，并对不同卡尔曼滤波变体进行了评估。研究结果表明，所提出的扰动观测器能以仅14毫秒的极小延迟准确重构驾驶员诱发的扰动。我们证明，在处理摩擦非线性时，非线性扩展卡尔曼滤波器的性能优于线性版本，在从静摩擦到动摩擦的过渡阶段改善了估计精度。鉴于本研究的方法论，不可避免地需依赖基于仿真的验证而非实际实验。未来研究需进一步探究该观测器在真实驾驶条件下的鲁棒性。