Adaptive Cooperative Control Strategy for a Wrist Exoskeleton Using Model-Based Joint Impedance Estimation

Adaptive Cooperative Control Strategy for a Wrist Exoskeleton Using Model-Based Joint Impedance Estimation
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使用基于模型的关节阻抗估计的手腕外骨骼自适应协作控制策略

DOI:
10.1109/tmech.2022.3211671
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发表时间:
2023-04
期刊:
IEEE/ASME Transactions on Mechatronics
影响因子:
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通讯作者:
Yihui Zhao;Kun Qian;Sheng Bo;Zhi-Li Zhang;Zhenhong Li;Guqiang Li;A. Dehghani-Sanij;Shengquan Xie
Yihui Zhao;Kun Qian;Sheng Bo;Zhi-Li Zhang;Zhenhong Li;Guqiang Li;A. Dehghani-Sanij;Shengquan Xie
中科院分区:
其他
文献类型:
--
作者:
Yihui Zhao;Kun Qian;Sheng Bo;Zhi-Li Zhang;Zhenhong Li;Guqiang Li;A. Dehghani-Sanij;Shengquan Xie

文献摘要

相似文献

腕部康复外骨骼在过去几十年中受到了广泛关注,致力于恢复神经肌肉疾病患者的运动功能。肌电信号被用来估计运动意图,以实现交互式训练方案。然而,真实的时间估计关节阻抗是一项具有挑战性的任务,因为它是用于控制外骨骼的关键参数。本文提出了一种基于实时关节阻抗估计方法的腕部外骨骼自适应协同控制策略。通过明确解释肌肉和骨骼系统中的潜在变换,所提出的方法同时估计人体受试者的运动意图和关节阻抗,而无需额外的校准程序,并相应地调节训练轨迹和辅助。结果表明,该方法优于其他训练方案,包括轨迹跟踪控制和固定协同控制。当估计的关节力矩增加12.36%时,所提出的控制策略提供了额外的66.25%的运动偏差,这提高了训练有效性和交互安全性,并促进了受试者的主动参与。
Wrist rehabilitation exoskeletons have gained much attention over the last decades, striving to restore motor functions for patients with neuromuscular disorders. Electromyography signal has been employed to estimate the motion intention to achieve interactive training schemes. However, it is a challenging task to estimate the joint impedance in real time, as it is a crucial parameter for control of exoskeletons. This article proposes an adaptive cooperative control strategy for a wrist exoskeleton based on a real-time joint impedance estimation approach. By explicitly interpreting the underlying transformation in the muscular and skeletal systems, the proposed approach estimates the motion intention and the joint impedance of a human subject simultaneously without additional calibration procedures and regulates the training trajectories and assistance accordingly. Results indicate the proposed method outperforms other training protocols, including the trajectory tracking control and the fixed cooperative control. The proposed control strategy provides an additional 66.25% motion deviation when estimated joint torque increases 12.36%, which enhances the training effectiveness and the interaction safety and promotes subjects' active engagement.