Figueiredo Joana, Fernandes Pedro Nuno, Moreno Juan C, Santos Cristina P
Center for MicroElectroMechanical Systems (CMEMS), University of Minho, Guimarães, Portugal.
LABBELS - Associate Laboratory, Braga/Guimarães, Portugal.
Anat Rec (Hoboken). 2023 Apr;306(4):728-740. doi: 10.1002/ar.25031. Epub 2022 Jul 23.
The use of exoskeletons in gait rehabilitation implies user-oriented and efficient responses of exoskeletons' controllers with adaptability for human-robot interaction. This study investigates the performance of a bioinspired hybrid control, the Feedback-Error Learning (FEL) controller, to time-effectively track user-oriented gait trajectories and adapt the exoskeletons' response to dynamic changes due to the interaction with the user. It innovates with a controller benchmarking analysis. FEL combines a proportional-integral-derivative (PID) feedback controller with a three-layer neural network feedforward controller that learns the inverse dynamics of the exoskeleton based on real-time feedback commands. FEL validation involved able-bodied subjects walking with knee and ankle exoskeletons at different gait speeds while considering gait disturbances. Results showed that the FEL control accurately (tracking error <7%) and timely (delay <30 ms) tracked gait trajectories. The feedforward controller learned the inverse dynamics of the exoskeletons in a time compliant for clinical use and adapted to variations in the gait trajectories, such as speed and position range, while the feedback controller compensated for random disturbances. FEL was more accurate and time-effective controller for tracking gait trajectories than a PID control (error <27%, delay <260 ms) and a lookup table feedforward combined with PID control (error <17%, delay >160 ms). These findings aligned with FEL's time-effectiveness favors its use in wearable exoskeletons for repetitive gait training.
在步态康复中使用外骨骼意味着外骨骼控制器要以用户为导向并做出高效响应，以适应人机交互。本研究调查了一种受生物启发的混合控制——反馈误差学习（FEL）控制器的性能，以有效地跟踪以用户为导向的步态轨迹，并使外骨骼的响应适应因与用户交互而产生的动态变化。它通过控制器基准分析进行创新。FEL将比例积分微分（PID）反馈控制器与三层神经网络前馈控制器相结合，该前馈控制器基于实时反馈命令学习外骨骼的逆动力学。FEL验证涉及健康受试者在不同步态速度下佩戴膝关节和踝关节外骨骼行走，同时考虑步态干扰。结果表明，FEL控制能够准确（跟踪误差<7%）且及时（延迟<30毫秒）地跟踪步态轨迹。前馈控制器在符合临床使用的时间内学习了外骨骼的逆动力学，并适应了步态轨迹的变化，如速度和位置范围，而反馈控制器则补偿了随机干扰。与PID控制（误差<27%，延迟<260毫秒）以及查找表前馈与PID控制相结合（误差<17%，延迟>160毫秒）相比，FEL是一种用于跟踪步态轨迹更准确且更具时效性的控制器。这些发现与FEL的时效性相符，有利于其在可穿戴外骨骼中用于重复步态训练。
