Schmitthenner Dave, Martin Anne E
Penn State, Mechanical Engineering, University Park, PA, USA.
R Soc Open Sci. 2021 Dec 22;8(12):211031. doi: 10.1098/rsos.211031. eCollection 2021 Dec.
While human walking has been well studied, the exact controller is unknown. This paper used human experimental walking data and system identification techniques to infer a human-like controller for a spring-loaded inverted pendulum (SLIP) model. Because the best system identification technique is unknown, three methods were used and compared. First, a linear system was found using ordinary least squares. A second linear system was found that both encoded the linearized SLIP model and matched the first linear system as closely as possible. A third nonlinear system used sparse identification of nonlinear dynamics (SINDY). When directly mapping states from the start to the end of a step, all three methods were accurate, with errors below 10% of the mean experimental values in most cases. When using the controllers in simulation, the errors were significantly higher but remained below 10% for all but one state. Thus, all three system identification methods generated accurate system models. Somewhat surprisingly, the linearized system was the most accurate, followed closely by SINDY. This suggests that nonlinear system identification techniques are not needed when finding a discrete human gait controller, at least for unperturbed walking. It may also suggest that human control of normal, unperturbed walking is approximately linear.
虽然人类行走已得到充分研究，但其确切的控制器尚不清楚。本文利用人体实验行走数据和系统辨识技术，为弹簧加载倒立摆（SLIP）模型推导了一种类似人类的控制器。由于最佳的系统辨识技术未知，因此使用并比较了三种方法。首先，使用普通最小二乘法找到一个线性系统。找到的第二个线性系统既对线性化的SLIP模型进行编码，又尽可能紧密地匹配第一个线性系统。第三个非线性系统使用非线性动力学的稀疏辨识（SINDY）。当直接将步长开始到结束的状态进行映射时，所有三种方法都很准确，在大多数情况下误差低于平均实验值的10%。在模拟中使用控制器时，误差显著更高，但除一种状态外，所有状态的误差仍低于10%。因此，所有三种系统辨识方法都生成了准确的系统模型。有点令人惊讶的是，线性化系统最准确，紧随其后的是SINDY。这表明在寻找离散的人类步态控制器时，至少对于无扰动行走，不需要非线性系统辨识技术。这也可能表明人类对正常、无扰动行走的控制近似线性。
