无需生理传感器，用运动数据实现软外骨骼的自适应动态助力。
A Task-Agnostic Control Strategy for Dynamic Assistance with Pneumatically Actuated Soft Exosuits
- 基于哈默斯坦模型建模人机系统，仅用运动信息推断用户意图。
- 在8°/s至120°/s速度下，交互扭矩降低73%，肌肉激活减少47%。
- 适合康复与日常辅助场景，不依赖肌电或力传感器。
气动人工肌肉为上肢软体外骨骼在康复、增强和日常辅助中提供了新可能。然而，其复杂的动力学特性与有限带宽，使得基于用户意图提供响应式助力长期面临挑战。本文提出一种基于逆植模型的控制策略，仅依赖运动传感，实现日常活动中无需任务特定设置的动态助力。通过哈默斯坦动态模型（包含 Preisach 滞后模型与线性时不变滤波器）刻画系统的静态与动态行为，并利用每位用户140秒的数据进行个性化建模，近似求解逆模型并嵌入控制回路。在模拟腕部助动外骨骼的测试平台上评估，当速度范围为8°/s至120°/s时，相比无助力状态，交互扭矩最大降低73%，关键屈肌与伸肌激活量最大减少47%。该方法无需生理或力传感器即可实现任务无关、动态响应的助力，显著提升外骨骼的实用性。
原文摘要 · Abstract (English)
Pneumatic artificial muscles have provided new opportunities to develop upper-extremity soft exosuits for reha- bilitation, augmentation, and assisted daily living. However, the complex dynamics and limited bandwidth of these actuators has made providing responsive assistance based on user intention a longstanding challenge. In this work, we present an inverse-plant control strategy for pneumatically actuated soft exosuits that only relies on kinematic sensing for task-agnostic and dynamic assistance during daily living. We model the human-robot system using a Hammerstein dynamic model, consisting of a Preisach hysteresis model and a linear time-invariant filter, to capture the static and dynamic behavior of the system. We personalize our model to each user using 140 s of data and approximate an inverse to integrate into our control loop. When evaluated on a test rig that emulated a soft assistive exosuit for the wrist, our controller reduced the interaction torque by up to 73% and the activation of key flexor and extensor muscles by up to 47% relative to the condition with no assistance for speeds ranging from 8°/s to 120°/s. Overall, this work presents a control strategy that can provide task-agnostic, dynamic assistance with pneumatically actuated soft exosuits without the need for physiological or force sensors to interpret user intention.
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