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JRM Vol.34 No.3 pp. 615-621
doi: 10.20965/jrm.2022.p0615
(2022)

Paper:

Human-Gait-Based Tracking Control for Lower Limb Exoskeleton Robot

Yongping Dan, Yifei Ge, Aihui Wang, and Zhuo Li

School of Electric and Information Engineering, Zhongyuan University of Technology
No. 41 Zhongyuan Road (M), Zhengzhou, Henan 450007, China

Corresponding author

Received:
September 6, 2021
Accepted:
January 28, 2022
Published:
June 20, 2022
Keywords:
lower limb exoskeleton robot, human gait data, radial basis function network, feed-forward control, motion capture
Abstract
Human-Gait-Based Tracking Control for Lower Limb Exoskeleton Robot

Control system

Research shows that it is practical for the normal human movement mechanism to assist the patients with stroke in robot-assisted gait rehabilitation. In passive training, the effect of rehabilitation training for patients can be improved by imitating normal human walking. To make the lower limb exoskeleton robot (LLER) move like a normal human, a tracking control scheme based on human gait data is proposed in this paper. The real human gait data is obtained from healthy subjects using a three-dimensional motion capture platform (3DMCP). Furthermore, the normal human motion characteristics are adopted to enhance the scientificity and effectiveness of assistant rehabilitation training using LLER. An adaptive radial basis function network (ARBFN) controller based on feed-forward control is presented to improve the trajectory tracking accuracy and tracking performance of the control system, where the ARBFN controller is deployed to predict the uncertain model parameters. The feed-forward controller based on the tracking errors is used to compensate for the input torque of LLER. The effectiveness of the presented control scheme is confirmed by simulation results based on experimental data.

Cite this article as:
Yongping Dan, Yifei Ge, Aihui Wang, and Zhuo Li, “Human-Gait-Based Tracking Control for Lower Limb Exoskeleton Robot,” J. Robot. Mechatron., Vol.34, No.3, pp. 615-621, 2022.
Data files:
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Last updated on Jun. 30, 2022