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

Kaiyang Yin, Kui Xiang, Muye Pang, Jing Chen, Philip Anderson, Longzhi Yang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)
43 Downloads (Pure)

Abstract

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.

Original languageEnglish
Article number8727447
Pages (from-to)72221-72233
Number of pages13
JournalIEEE Access
Volume7
Early online date31 May 2019
DOIs
Publication statusPublished - 13 Jun 2019

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