基于人体生物力学阻抗辨识的月面建造远程变阻抗控制
doi: 10.11728/cjss2026.03.2025-0142 cstr: 32142.14.cjss.2025-0142
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冯籽越 男, 2001年10月出生于浙江省金华市, 博士生, 主要研究方向为面向月面建造的人机交互、遥操作等. E-mail: 24B918067@stu.hit.edu.cn -
袁帅 男, 1988年10月出生于湖南省常德市, 现为飞行器动力学与控制研究所副所长, 教授, 博士生导师, 主要研究方向为轻量化智能优化算法、飞行器控制与规划, 以及未来月球科研站建造基础理论和关键技术等方面. E-mail: shuaiyuan@hit.edu.cn
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Teleimpedance Control for Lunar Construction Based on Biomechanical Impedance Identification of Human Body
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摘要: 随着月球探索的不断发展, 针对月面基地建设及设施建造等任务需求, 远端机械臂在不确定、非结构化环境下的交互行为对安全性、准确性和透明度提出更高要求. 仿人变阻抗控制方法能够在兼顾环境交互力安全的同时实现高精度的控制, 为解决月面人机协同难题提供了新思路. 使用基于人体阻抗参数的远程变阻抗机械臂控制策略, 将人体变阻抗参数映射到远端机械臂, 满足月面建造任务的复杂交互需求. 该方法通过融合4通道表面肌电信号与上肢肌肉力学模型, 构建人体末端刚度实时辨识系统. 为解决仿人变阻抗的泛化问题, 引入个性化人体物理参数, 克服了传统测量方法的局限性并提升了泛化性能. 同时, 在远程变阻抗控制中, 通过力觉、视觉反馈提升交互过程中的信息透明度, 并利用人体自然神经反射实现阻抗的自适应调整. 进而, 基于月面桁架建设平台的装配任务需求, 验证了远程变阻抗控制相较于传统遥操作方案的优势.Abstract: Driven by the continuous progress in lunar exploration, teleoperated robotic arms require highly safe, accurate, and transparent control strategies to handle uncertain and unstructured environments during lunar base construction. Humanoid variable impedance control ensures both safe environmental interaction and high-precision tracking, providing a robust solution for human-robot collaboration. This study investigates a teleoperation strategy that maps human impedance parameters onto a remote robotic arm to meet the interactive demands of lunar tasks. By integrating four-channel surface ElectroMyoGraphy (sEMG) signals with an upper limb mechanics model (built upon Hill’s model and kinematics), a real-time identification system for human end-effector stiffness is established. Unlike conventional methods, this strategy incorporates personalized physical parameters to enhance the generalization of humanoid impedance control. Furthermore, force and visual feedback are utilized to improve information transparency and leverage natural neural reflexes for adaptive impedance adjustment. Finally, experimental results on a lunar truss assembly platform demonstrate that the proposed humanoid variable impedance control significantly outperforms traditional teleoperation schemes.
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图 3 不同肌肉激活程度下肌肉张力与长度的关系
Figure 3. Muscle tension-length relationship under different muscle activation
图 7 基于波变量的时延补偿控制架构
Figure 7. Time-delay compensation control architecture based on wave variables
图 9 不同角度下各块肌肉肌力与最大自主收缩百分比的关系
Figure 9. Specific muscle force and PMVC relationships across different joint angles
图 10 不同角度下屈曲刚度与最大自主收缩百分比的关系
Figure 10. Relationship between flexion stiffness and PMVC at different angles
图 11 不同角度下伸展刚度与最大自主收缩百分比的关系
Figure 11. Relationship between extension stiffness and PMVC at different angles
图 12 月面桁架结构变阻抗装配实验从端平台
Figure 12. Experimental platform for variable impedance assembly of lunar truss structure
图 13 不同控制策略下轴向位移修正 (a) 与环境力 (b) 的时变特征对比 (虚线为两次实验的节点)
Figure 13. Comparison of time-varying axial displacement correction (a) and environmental contact force (b) under different control strategies (The dashed line is a node of the two experiments)
图 14 2 s时延下恒定阻抗与可变阻抗控制策略的位移跟踪性能对比
Figure 14. Comparison of displacement tracking performance under constant and variable impedance control strategies with a 2 s time delay
图 15 2 s时延不同阻抗策略下的位移跟踪误差
Figure 15. Displacement tracking error under different impedance strategies with 2 s delay
表 1 肌肉力学模型拟合参数辨识结果
Table 1. Identification results of muscle model fitting parameters
肌肉 $ \varphi ({\tilde{s}}_{i}) $ $ \eta ({l}_{i}) $ $ \omega ({l}_{i}) $ $ \beta $ $ \gamma $ $ a $ $ b $ $ c $ $ d $ 二头肌 0.7 80 6.8×105 –20.5×105 20.6×105 –6.9×105 肱桡肌 0.7 75 0.27×105 –0.80×105 0.81×105 –0.27×105 三头肌长头 0.8 180 –1.1×105 2.9×105 –2.6×105 0.76×105 三头肌外侧头 0.7 365 0.67×105 –2.1×105 2.2×105 –0.74×105 
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