Humans can rapidly change their low-frequency arm dynamics to resist forces or give way to them. Quantifying driver time-varying arm dynamics is important to develop steer-by-wire and haptic support systems. Conventional linear time-invariant (LTI) identification, and even time-varying techniques such as wavelets, fail to capture fast changing dynamics. Moreover, such techniques require perturbation signals on the steering wheel (SW), which may affect steering feel and control behavior. We propose a novel two-step method to estimate time-varying driver admittance, using unobtrusive grip-force measurements of the hands on the wheel to schedule a linear parameter-varying (LPV) model that captures the full admittance range. A total of 18 subjects participated in two experiments in a simulator with an actuated SW. In a sensorimotor control experiment, we first establish the grip force and admittance relationship, requiring subjects to perform a boundary tracking task where perturbations on the wheel enabled local LTI identification. Six boundary widths is used to evoke admittance changes, after which a global LPV model is obtained through interpolation between the local models. Results show an inverse relationship between grip force and admittance and that the LPV model accurately captures the admittance settings (fit percentage > 90%). Second, a driving experiment is followed that aims to evoke differences in grip force and admittance in response to varying road widths, offering more realistic data to evaluate the LPV model predictions. Results show that the LPV model accurately describes adaptations in admittance to road width. Our method allows for online estimation of time-varying admittance during driving, without applying force perturbations.

中文翻译：

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使用握力作为调度变量估计驾驶员神经肌肉导纳的 LPV 模型


人类可以快速改变其低频手臂动力学以抵抗力或屈服于力。量化驾驶员随时间变化的手臂动力学对于开发线控转向和触觉支持系统非常重要。传统的线性时不变（LTI）识别，甚至小波等时变技术，都无法捕捉快速变化的动态。此外，此类技术需要方向盘（SW）上的扰动信号，这可能会影响转向感觉和控制行为。我们提出了一种新颖的两步方法来估计随时间变化的驾驶员导纳，使用方向盘上的手的不显眼的握力测量来安排捕获整个导纳范围的线性参数变化（LPV）模型。共有 18 名受试者参与了带有驱动 SW 的模拟器中的两项实验。在感觉运动控制实验中，我们首先建立握力和导纳关系，要求受试者执行边界跟踪任务，其中车轮上的扰动能够实现局部 LTI 识别。使用六个边界宽度来引发导纳变化，然后通过局部模型之间的插值获得全局 LPV 模型。结果显示握力与导纳之间存在反比关系，并且 LPV 模型准确地捕获了导纳设置（拟合百分比 > 90%）。其次，进行了驾驶实验，旨在根据不同的道路宽度引起抓地力和导纳的差异，从而提供更真实的数据来评估 LPV 模型的预测。结果表明，LPV 模型准确地描述了道路宽度导纳的适应性。我们的方法允许在线估计驾驶过程中随时间变化的导纳，而无需施加力扰动。