Single Leg Gait Tracking of Lower Limb Exoskeleton Based on Adaptive Iterative Learning Control

The lower limb exoskeleton is a wearable human-robot interactive equipment, which is tied to human legs and moves synchronously with the human gait. Gait tracking accuracy greatly affects the performance and safety of the lower limb exoskeletons. As the human-robot coupling systems are usually nonlinear and generate unpredictive errors, a conventional iterative controller is regarded as not suitable for safe implementation. Therefore, this study proposed an adaptive control mechanism based on the iterative learning model to track the single leg gait for lower limb exoskeleton control. To assess the performance of the proposed method, this study implemented the real lower limb gait trajectory that was acquired with an optical motion capturing system as the control inputs and assessment benchmark. Then the impact of the human-robot interaction torque on the tracking error was investigated. The results show that the interaction torque has an inevitable impact on the tracking error and the proposed adaptive iterative learning control (AILC) method can effectively reduce such error without sacrificing the iteration efficiency.