Zhong, B, Cao, J, Guo, K et al. (5 more authors) (2020) Fuzzy logic compliance adaptation for an assist-as-needed controller on the Gait Rehabilitation Exoskeleton (GAREX). Robotics and Autonomous Systems, 133. 103642. ISSN 0921-8890
Abstract
Assist-as-needed control strategy is an emerging approach to improve the effectiveness of gait rehabilitation training. We have proposed a pneumatic muscle (PM) driven Gait Rehabilitation Exoskeleton (GAREX) implemented with a multi-input–multi-output (MIMO) sliding mode control system to actively adjust the assistance level provided during gait rehabilitation. To realize the assist-as-needed control strategy, a specific algorithm is imperative to assess the active participation or effort of wearers and adapt the amount of assistance accordingly. We sought to establish a fuzzy logic compliance adaptation (FLCA) controller to form a novel cascade control system. We evaluated the feasibility of implemented FLCA controller on the performance of adjusting the compliance of GAREX’s knee joint according to the online assessment of the wear’s active participation level once in every gait cycle. Using controlled, treadmill-based walking tests involved three healthy subjects, we demonstrate that FLCA controller could effectively distinguish the capability/effort levels of wearers and enable the exoskeleton to adapt the knee joint compliance accordingly. Obtained results reveal that FLCA controller can collaborate well with MIMO sliding mode controller in a system and indicate the novel method of realizing assist-as-needed concept with the pneumatic muscle powered mechanisms.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | ©2020 Elsevier B.V. All rights reserved. This is an author produced version of a paper published in Robotics and Autonomous Systems. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Fuzzy logic; Pneumatic muscle; Compliance adaptation; Gait rehabilitation; Assist-as-needed |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds) |
Funding Information: | Funder Grant number Royal Society ICA\R1\180203 |
Depositing User: | Symplectic Publications |
Date Deposited: | 18 Sep 2020 12:55 |
Last Modified: | 03 Sep 2021 00:40 |
Status: | Published |
Publisher: | Elsevier BV |
Identification Number: | 10.1016/j.robot.2020.103642 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:165629 |