Although human–exoskeleton systems have tremendous potentials for rehabilitation training, transparent human–exoskeleton interaction has not been achieved for late-phase rehabilitation. To cope with this challenge, an adaptive pilot intent prediction–based control algorithm is proposed to achieve compliant motion coordination. A novel surface electromyography signal processing method providing definite physical meaning and high computational efficiency is derived. The state space model of human musculoskeletal system is developed with the input of the processed surface electromyography. Adaptive parameter estimation is employed to cope with the emerging dynamics. It integrates the advantages of surface electromyography signals which imply muscle contraction in advance and force signals with high stability. Based on the coupled human–exoskeleton system dynamics, transparent control of a human–exoskeleton system is realized. Experimental results show that the computational efficiency of the proposed surface electromyography processing method is 129% higher than the energy kernel method, and the maximum interactive force is reduced about 15 N compared with impedance control.

中文翻译：

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基于自适应运动意图理解的人外骨骼系统控制

尽管人-外骨骼系统在康复训练方面具有巨大潜力，但在后期康复中尚未实现透明的人-外骨骼交互。为了应对这一挑战，提出了一种基于自适应飞行员意图预测的控制算法来实现柔顺的运动协调。推导出一种具有明确物理意义和高计算效率的新型表面肌电信号处理方法。人体肌肉骨骼系统的状态空间模型是在处理后的表面肌电信号的输入下开发的。采用自适应参数估计来应对新出现的动态。它综合了表面肌电信号提前暗示肌肉收缩和力信号稳定性高的优点。基于耦合的人-外骨骼系统动力学，实现人-外骨骼系统的透明控制。实验结果表明，所提出的表面肌电处理方法的计算效率比能量核方法提高了129%，与阻抗控制相比，最大交互力降低了约15 N。