TY - JOUR
T1 - Personalised Control of Robotic Ankle Exoskeleton Through Experience-Based Adaptive Fuzzy Inference
AU - Yin, Kaiyang
AU - Xiang, Kui
AU - Pang, Muye
AU - Chen, Jing
AU - Anderson, Philip
AU - Yang, Longzhi
PY - 2019/6/13
Y1 - 2019/6/13
N2 - 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.
AB - 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.
KW - adaptive fuzzy rule interpolation
KW - muscle-tendon complex model
KW - rehabilitation support
KW - Robotic ankle exoskeleton
U2 - 10.1109/ACCESS.2019.2920134
DO - 10.1109/ACCESS.2019.2920134
M3 - Article
AN - SCOPUS:85067402811
SN - 2169-3536
VL - 7
SP - 72221
EP - 72233
JO - IEEE Access
JF - IEEE Access
M1 - 8727447
ER -