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JRM Vol.33 No.1 pp. 88-96
doi: 10.20965/jrm.2021.p0088
(2021)

Paper:

Human-Like Robust Adaptive PD Based Human Gait Tracking for Exoskeleton Robot

Aihui Wang*, Ningning Hu**, Jun Yu***, Junlan Lu*, Yifei Ge*, and Yan Wang*

*School of Electric and Information Engineering, Zhongyuan University of Technology
No.41 Zhongyuan Road, Zhengzhou 450007, China

**School of Mechatronic Engineering and Automation, Shanghai University
No.99 Shangda Road, Shanghai 200444, China

***Zhongyuan-Petersburg Aviation College, Zhongyuan University of Technology
No.41 Zhongyuan Road, Zhengzhou 450007, China

Received:
January 20, 2020
Accepted:
September 12, 2020
Published:
February 20, 2021
Keywords:
motion capture, human gait trajectory, robust adaptive PD, parameters identification
Abstract
Human-Like Robust Adaptive PD Based Human Gait Tracking for Exoskeleton Robot

NOKOV 3D motion capture system

For patients with dyskinesias caused by central nervous system diseases such as stroke, in the early stage of rehabilitation training, lower limb rehabilitation robots are used to provide passive rehabilitation training. This paper proposed a human-like robust adaptive PD control strategy of the exoskeleton robot based on healthy human gait data. When the error disturbance is bounded, a human-like robust adaptive PD control strategy is designed, which not only enables the rehabilitation exoskeleton robot to quickly track the human gait trajectory obtained through the 3D NOKOV motion capture system, but also can well identify the structural parameters of the system and avoid excessively initial output torque for the robot. MATLAB simulation verifies that the proposed method has a better performance to realize tracking the experimental trajectory of human movement and anti-interference ability under the condition of ensuring global stability for a lower limb rehabilitation exoskeleton robot.

Cite this article as:
Aihui Wang, Ningning Hu, Jun Yu, Junlan Lu, Yifei Ge, and Yan Wang, “Human-Like Robust Adaptive PD Based Human Gait Tracking for Exoskeleton Robot,” J. Robot. Mechatron., Vol.33, No.1, pp. 88-96, 2021.
Data files:
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Last updated on Mar. 01, 2021