用自适应控制让外骨骼在沙地上帮人省力走路
Assistive Control of Knee Exoskeletons for Human Walking on Granular Terrains
- 基于地面反作用力预测，动态调节外骨骼刚度
- 实测沙地行走时肌肉激活降15%，耗能降3.7%
- 适合户外复杂地形下助行设备研发者
人在沙地等颗粒状地形上行走时，步态和能耗明显不同于硬质地面。本文提出一种基于刚度的模型预测控制方法，用于沙地环境下膝部外骨骼的辅助控制。首先对比分析了人类在沙地与硬质地面的步态与运动特征；随后设计了一种基于机器学习的实时估计方案，用于预测不同地形下的地面反作用力（GRFs）；结合估算的GRFs与人体关节扭矩，构建了基于模型预测刚度控制的膝外骨骼控制器。通过室内外实验验证了建模与控制设计的有效性。实验结果表明，在沙地行走时，该辅助系统使主要肌群激活降低15%，代谢消耗减少3.7%。
原文摘要 · Abstract (English)
Human walkers traverse diverse environments and demonstrate different gait locomotion and energy cost on granular terrains compared to solid ground. We present a stiffness-based model predictive control approach of knee exoskeleton assistance on sand. The gait and locomotion comparison is first discussed for human walkers on sand and solid ground. A machine learning-based estimation scheme is then presented to predict the ground reaction forces (GRFs) for human walkers on different terrains in real time. Built on the estimated GRFs and human joint torques, a knee exoskeleton controller is designed to provide assistive torque through a model predictive stiffness control scheme. We conduct indoor and outdoor experiments to validate the modeling and control design and their performance. The experiments demonstrate the major muscle activation and metabolic reductions by respectively 15% and 3.7% under the assistive exoskeleton control of human walking on sand.
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