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一种基于自适应迭代学习的机器人辅助上肢被动康复阻抗控制方法

An Adaptive Iterative Learning Based Impedance Control for Robot-Aided Upper-Limb Passive Rehabilitation.

作者信息

Ting Wang, Aiguo Song

机构信息

School of Instrument Science and Engineering, Southeast University, Nanjing, China.

出版信息

Front Robot AI. 2019 Jun 4;6:41. doi: 10.3389/frobt.2019.00041. eCollection 2019.

DOI:10.3389/frobt.2019.00041
PMID:33501057
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7806107/
Abstract

In this paper, an anthropomorphic arm is introduced and used to the upper-limb passive rehabilitation therapy. The anthropomorphic arm is constructed via pneumatic artificial muscles so that it may assist patients suffering upper-limb diseases to achieve mild therapeutic exercises. Due to the uncertain dynamic environment, external disturbances and model uncertainties, a combined control is proposed to stabilize and to enhance the adaptivity of the system. In the combined control, an iterative learning control is used to realize accurate position tracking. Meanwhile, an adaptive iterative learning based impedance control is proposed to execute the appropriate contact force during the therapy of the upper-limb. The advantage of the combined control is that it doesn't depend on the accurate model of systems and it may deal with highly nonlinear system which has strong coupling and redundancies. The convergence of the proposed control is analyzed in detail. Numerical simulations are performed to verify the proposed control method. In addition, real experiments are executed on the Southwest anthropomorphic arm.

摘要

本文介绍了一种拟人手臂，并将其应用于上肢被动康复治疗。该拟人手臂通过气动人工肌肉构建，以便辅助上肢疾病患者进行轻度治疗性锻炼。由于动态环境不确定、存在外部干扰和模型不确定性，提出了一种组合控制方法来稳定系统并提高其适应性。在组合控制中，采用迭代学习控制来实现精确的位置跟踪。同时，提出了一种基于自适应迭代学习的阻抗控制方法，以便在上肢治疗过程中施加适当的接触力。组合控制的优点是不依赖于系统的精确模型，并且可以处理具有强耦合和冗余的高度非线性系统。详细分析了所提出控制方法的收敛性。进行了数值模拟以验证所提出的控制方法。此外，还在西南拟人手臂上进行了实际实验。

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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9dd9/7806107/b62841213c0e/frobt-06-00041-g0006.jpg
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