TY - GEN
T1 - Robust Control of Lower Extremity Exoskeleton Rehabilitation Robot Based on Nominal Modeling
AU - Song, Zhuangqun
AU - Zhao, Peng
AU - Yang, Rong
AU - Wu, Xiangning
AU - Yang, Chao
AU - Gao, Xueshan
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - This research tackles the issue of controlling joint angle tracking in lower extremity exoskeleton rehabilitation robots by presenting a method known as Robust Control of Lower Extremity Exoskeleton Rehabilitation Robots based on the Nominal Model (RC-LEERR-NM). Initially, the dynamics model for the single-leg swing phase of the exoskeleton was developed using the Lagrange method, followed by a thorough analysis of the exoskeleton's gait. The dynamics model was then divided into two components: the nominal model and the robust control, which together form the control terms necessary for the effective management of the exoskeleton system. Additionally, an integral-type Lyapunov function was created to demonstrate that the proposed control system ensures global asymptotic stability. The method's effectiveness was confirmed through simulations and experimental results, which indicated that the RC-LEERRNM method significantly outperforms PID control in terms of dynamic stability and trajectory tracking accuracy, successfully enabling the lower extremity exoskeleton to accurately follow human gait patterns.
AB - This research tackles the issue of controlling joint angle tracking in lower extremity exoskeleton rehabilitation robots by presenting a method known as Robust Control of Lower Extremity Exoskeleton Rehabilitation Robots based on the Nominal Model (RC-LEERR-NM). Initially, the dynamics model for the single-leg swing phase of the exoskeleton was developed using the Lagrange method, followed by a thorough analysis of the exoskeleton's gait. The dynamics model was then divided into two components: the nominal model and the robust control, which together form the control terms necessary for the effective management of the exoskeleton system. Additionally, an integral-type Lyapunov function was created to demonstrate that the proposed control system ensures global asymptotic stability. The method's effectiveness was confirmed through simulations and experimental results, which indicated that the RC-LEERRNM method significantly outperforms PID control in terms of dynamic stability and trajectory tracking accuracy, successfully enabling the lower extremity exoskeleton to accurately follow human gait patterns.
KW - lower extremity exoskeleton rehabilitation robot
KW - nominal dynamics modeling
KW - robust control
KW - trajectory tracking
UR - http://www.scopus.com/inward/record.url?scp=85218121150&partnerID=8YFLogxK
U2 - 10.1109/ICCD62811.2024.10843439
DO - 10.1109/ICCD62811.2024.10843439
M3 - Conference contribution
AN - SCOPUS:85218121150
T3 - 2024 IEEE International Conference on Cognitive Computing and Complex Data, ICCD 2024
SP - 306
EP - 313
BT - 2024 IEEE International Conference on Cognitive Computing and Complex Data, ICCD 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE International Conference on Cognitive Computing and Complex Data, ICCD 2024
Y2 - 28 September 2024 through 30 September 2024
ER -