Abstract：
The lower limb rehabilitation robot is a typical man-machine coupling system. Aiming at the problems of insufficient physiological information and unsatisfactory safety performance in the compliance control strategy for the lower limb rehabilitation robot during passive training, this study developed a surface electromyography-based gain-tuned compliance control (EGCC) strategy for the lower limb rehabilitation robot. First, the mapping function relationship between the normalized surface electromyography (sEMG) signal and the gain parameter was established and an overall EGCC strategy proposed. Next, the EGCC strategy without sEMG information was simulated and analyzed. The effects of the impedance control parameters on the position correction amount were studied, and the change rules of the robot end trajectory, man-machine contact force, and position correction amount analyzed in different training modes. Then, the sEMG signal acquisition and feature analysis of target muscle groups under different training modes were carried out. Finally, based on the lower limb rehabilitation robot control system, the influence of normalized sEMG threshold on the robot end trajectory and gain parameters under different training modes was experimentally studied. The simulation and experimental results show that the adoption of the EGCC strategy can significantly enhance the compliance of the robot end-effector by detecting the sEMG signal and improve the safety of the robot in different training modes, indicating the EGCC strategy has good application prospects in the rehabilitation robot field.
摘要：
下肢康复机器人是典型的人机耦合系统。针对下肢康复机器人被动训练时的顺应性控制策略存在生理信息不足、安全性能不理想等问题,这项研究为下肢康复机器人开发了一种基于表面肌电图的增益调节顺应性控制（EGCC）策略。首先,建立了归一化表面肌电图（sEMG）信号与增益参数之间的映射函数关系，并提出了整体EGCC策略。接下来,对没有sEMG信息的EGCC策略进行了模拟和分析。研究了阻抗控制参数对位置校正量的影响,以及机器人末端轨迹的变化规律，人机接触力,分析了不同训练模式下的位置校正量。然后，对不同训练模式下的目标肌群进行sEMG信号采集和特征分析。最后,基于下肢康复机器人控制系统,实验研究了不同训练模式下归一化sEMG阈值对机器人末端轨迹和增益参数的影响。仿真和实验结果表明，采用EGCC策略可以通过检测sEMG信号显著增强机器人末端执行器的顺应性，提高机器人在不同训练模式下的安全性。说明EGCC策略在康复机器人领域具有良好的应用前景。
