Abstract
This article introduces a human–robot interaction controller toward the lower extremity exoskeleton whose aim is to improve the tracking performance and drive the exoskeleton to shadow the wearer with less interaction force. To acquire the motion intention of the wearer, two subsystems are designed: the first is to infer the wearer is in which phase based on floor reaction force detected by a multi-sensor system installed in the sole, and the second is to infer the motion velocity based on the multi-axis force sensor and admittance model. An improved single-input fuzzy sliding mode controller is designed, and the adaptive switching controller is combined to promote the tracking performance considering system uncertainties. Adaptation laws are designed based on the Lyapunov stability theorem. Therefore, the stability of the single-input adaptive fuzzy sliding mode control can be guaranteed. Finally, the proposed methods are applied to the lower extremity exoskeleton, especially in the swing phase. Its effectiveness is validated by comparative experiments.
Highlights
Lower extremity exoskeleton is a typical kind of integrated human–robot system which is superior to any autonomous robotic system in unstructured environments that demand significant adaptation.[1]
We have identified the four walking phases even if the floor reaction force (FRF) of the foot front part is omitted
The estimation algorithm based on FRF which can reflect wearer’s motion status is investigated through the walking experiment
Summary
Lower extremity exoskeleton is a typical kind of integrated human–robot system which is superior to any autonomous robotic system in unstructured environments that demand significant adaptation.[1].
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