改进的滤波器让外骨骼更准地对抗未知干扰。
Improved Extended Kalman Filter-Based Disturbance Observers for Exoskeletons
- 用多模型和核相关性增强滤波器，提升干扰估计精度。
- 实测显示髋膝关节误差降低36.3%与46.3%。
- 适合需要高精度力控的外骨骼与康复机器人应用。
机械系统常受未知干扰影响性能。双自由度控制结构可分离性能与抗扰要求，但干扰动态未知时无法完全消除干扰。本文揭示了跟踪速度与跟踪不确定性之间存在的固有权衡。为此提出两种新方法：基于交互多模型扩展卡尔曼滤波的干扰观测器，以及基于多核相关性扩展卡尔曼滤波的干扰观测器。在下肢外骨骼实验中，相较于传统扩展卡尔曼滤波观测器，所提方法在时变交互力场景下分别将髋关节误差降低36.3%和16.2%，膝关节误差降低46.3%和24.4%，验证了其优越性。
原文摘要 · Abstract (English)
The nominal performance of mechanical systems is often degraded by unknown disturbances. A two-degree-of-freedom control structure can decouple nominal performance from disturbance rejection. However, perfect disturbance rejection is unattainable when the disturbance dynamic is unknown. In this work, we reveal an inherent trade-off in disturbance estimation subject to tracking speed and tracking uncertainty. Then, we propose two novel methods to enhance disturbance estimation: an interacting multiple model extended Kalman filter-based disturbance observer and a multi-kernel correntropy extended Kalman filter-based disturbance observer. Experiments on an exoskeleton verify that the proposed two methods improve the tracking accuracy $36.3\%$ and $16.2\%$ in hip joint error, and $46.3\%$ and $24.4\%$ in knee joint error, respectively, compared to the extended Kalman filter-based disturbance observer, in a time-varying interaction force scenario, demonstrating the superiority of the proposed method.
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