论文标题
以人为中心的下肢机器人辅助的模型预测控制
Model Predictive Control for Human-Centred Lower Limb Robotic Assistance
论文作者
论文摘要
神经创伤导致的流动性或平衡丧失是公共卫生的关键考虑因素。机器人外骨骼具有康复和辅助运动的巨大潜力，但考虑到患者的病理差异，需要辅助（AAN）控制仍未解决。我们引入了一个模型预测控制（MPC）体系结构，用于围绕模糊逻辑算法（FLA）识别基于人类参与的辅助模式的下肢外骨骼。援助模式是：1）被动的人类放松和机器人主导地位，2）主动协助与该任务合作，3）在人类对机器人的抵抗的情况下。人扭矩是根据肌电图（EMG）信号估算的，在关节运动之前，可以通过MPC进行扭矩的高级预测，并选择了FLA的辅助模式。在硬件中证明了该控制器，其中三个受试者在膝关节外骨骼上有三个受试者，可追踪正弦轨迹，具有人类放松的辅助功能和抵抗力。实验结果表明，在辅助模式之间快速且适当的转移，并在每种模式下满足辅助性能。结果说明了一种客观的方法，可以通过运动模式之间的直立过渡来进行下肢机器人援助，从而提供了新水平的人类机器人协同作用，以进行移动辅助和康复。
Loss of mobility or balance resulting from neural trauma is a critical consideration in public health. Robotic exoskeletons hold great potential for rehabilitation and assisted movement, yet optimal assist-as-needed (AAN) control remains unresolved given pathological variance among patients. We introduce a model predictive control (MPC) architecture for lower limb exoskeletons centred around a fuzzy logic algorithm (FLA) identifying modes of assistance based on human involvement. Assistance modes are: 1) passive for human relaxed and robot dominant, 2) active-assist for human cooperation with the task, and 3) safety in the case of human resistance to the robot. Human torque is estimated from electromyography (EMG) signals prior to joint motions, enabling advanced prediction of torque by the MPC and selection of assistance mode by the FLA. The controller is demonstrated in hardware with three subjects on a 1-DOF knee exoskeleton tracking a sinusoidal trajectory with human relaxed assistive, and resistive. Experimental results show quick and appropriate transfers among the assistance modes and satisfied assistive performance in each mode. Results illustrate an objective approach to lower limb robotic assistance through on-the-fly transition between modes of movement, providing a new level of human-robot synergy for mobility assist and rehabilitation.
