一种用于气动驱动软外服的任务无关型动态助力控制策略

中文摘要 AI 辅助

气动人工肌肉为开发用于康复、增强和日常生活辅助的上肢软外服提供了新机遇，但这类执行器的复杂动力学和有限带宽，使得基于用户意图提供响应式助力成为长期挑战。本研究提出一种用于气动驱动软外服的逆植物控制策略，仅依赖运动学传感即可在日常生活中实现任务无关型动态助力。我们采用Hammerstein动态模型对人-机器人系统建模，该模型由Preisach迟滞模型和线性时不变滤波器组成，以捕捉系统的静态与动态行为；利用140秒的数据对每个用户的模型进行个性化调整，并近似其逆模型以整合至控制回路中。在模拟腕部软助力外服的测试台上进行评估时，与无助力情况相比，该控制器在8°/s至120°/s的速度范围内，可将交互扭矩降低多达73%，并将主要屈肌和伸肌的激活程度降低多达47%。总体而言，本研究提出了一种控制策略，无需生理传感器或力传感器来解读用户意图，即可为气动驱动软外服提供任务无关型动态助力。

英文摘要

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.

发表机构

• University of California San Diego（加利福尼亚大学圣迭戈分校）

机构由 AI 辅助整理，请以论文原文为准。