Personalised Control of Robotic Ankle Exoskeleton Through Experience-Based Adaptive Fuzzy Inference

Yin, Kaiyang, Xiang, Kui, Pang, Muye, Chen, Jing, Anderson, Philip and Yang, Longzhi (2019) Personalised Control of Robotic Ankle Exoskeleton Through Experience-Based Adaptive Fuzzy Inference. IEEE Access, 7. pp. 72221-72233. ISSN 2169-3536

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Robotic exoskeletons have emerged as effective rehabilitation and ability-enhancement tools, by mimicking or supporting natural body movements. The control schemes of exoskeletons are conventionally developed based on fixed torque-ankle state relationship or various human models, which are often lack of flexibility and adaptability to accurately address personalized movement assistance needs. This paper presents an adaptive control strategy for personalized robotic ankle exoskeleton in an effort to address this limitation. The adaptation was implemented by applying the experience-based fuzzy rule interpolation approach with the support of a muscle-tendon complex model. In particular, this control system is initialized based on the most common requirements of a 'standard human model,' which is then evolved during its performance by effectively using the feedback collected from the wearer to support different body shapes and assistance needs. The experimental results based on different human models with various support demands demonstrate the power of the proposed control system in improving the adaptability, and thus applicability, of robotic ankle exoskeletons.

Item Type: Article
Uncontrolled Keywords: adaptive fuzzy rule interpolation, muscle-tendon complex model, rehabilitation support, Robotic ankle exoskeleton
Subjects: F200 Materials Science
G400 Computer Science
H100 General Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Elena Carlaw
Date Deposited: 04 Jul 2019 15:04
Last Modified: 01 Aug 2021 11:18

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