Feedback-Error Learning for time-effective gait trajectory tracking in wearable exoskeletons.

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.