Iterative learning-based path control for robot-assisted upper-limb rehabilitation

Maqsood, Kamran, Luo, Jing, Yang, Chenguang, Ren, Qinyuan and Li, Yanan (2021) Iterative learning-based path control for robot-assisted upper-limb rehabilitation. Neural Computing and Applications. ISSN 0941-0643

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In robot-assisted rehabilitation, the performance of robotic assistance is dependent on the human user’s dynamics, which are subject to uncertainties. In order to enhance the rehabilitation performance and in particular to provide a constant level of assistance, we separate the task space into two subspaces where a combined scheme of adaptive impedance control and trajectory learning is developed. Human movement speed can vary from person to person and it cannot be predefined for the robot. Therefore, in the direction of human movement, an iterative trajectory learning approach is developed to update the robot reference according to human movement and to achieve the desired interaction force between the robot and the human user. In the direction normal to the task trajectory, human’s unintentional force may deteriorate the trajectory tracking performance. Therefore, an impedance adaptation method is utilized to compensate for unknown human force and prevent the human user drifting away from the updated robot reference trajectory. The proposed scheme was tested in experiments that emulated three upper-limb rehabilitation modes: zero interaction force, assistive and resistive. Experimental results showed that the desired assistance level could be achieved, despite uncertain human dynamics.

Item Type: Article
Schools and Departments: School of Engineering and Informatics > Engineering and Design
SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 02 Mar 2021 08:48
Last Modified: 28 Feb 2022 13:27

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Project NameSussex Project NumberFunderFunder Ref
The Game Theory of Human-Robot Interaction - HRIgameG2929EPSRC-ENGINEERING & PHYSICAL SCIENCES RESEARCH COUNCILUnset