Abstract

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.

Highlights

  • IntroductionLower limb exoskeleton robot is a mechanical device to improve a human’s “physical strength” [1,2]

  • Lower limb exoskeleton robot is a mechanical device to improve a human’s “physical strength” [1,2].It can assist aged and disabled citizens, rehabilitate injured patients, and extend the capacity of military and engineering forces [3,4,5,6]

  • This paper proposed an adaptive iterative learning control mechanism to track the gait trajectory of the lower limb exoskeleton

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Summary

Introduction

Lower limb exoskeleton robot is a mechanical device to improve a human’s “physical strength” [1,2]. It can assist aged and disabled citizens, rehabilitate injured patients, and extend the capacity of military and engineering forces [3,4,5,6]. As the lower limb exoskeleton should move synchronously with human legs, accurate tracking of human gait is important for its performance [7,8,9]. ILC is suitable for lower limb exoskeleton control with proper gait trajectory tracking. Wang et al designed an ILC algorithm with a forgetting factor to improve the control system robustness at the cost of response speed [13]

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