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Model identification and admittance control with neighborhood field optimization in human-exoskeleton cooperative motion

Published online by Cambridge University Press:  22 December 2025

Haoran Zhan ,
Jiange Kou ,
Yuanchao Cao ,
Wanqi Wang ,
Jiyu Zhang Open the ORCID record for Jiyu Zhang [Opens in a new window] ,
Yan Shi  and
Qing Guo Open the ORCID record for Qing Guo [Opens in a new window]
Haoran Zhan
Affiliation:
School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, China
Jiange Kou
Affiliation:
School of Automation Science and Electrical Engineering, Beihang University (BUAA), Beijing, China
Yuanchao Cao
Affiliation:
School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, China
Wanqi Wang
Affiliation:
School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, China
Jiyu Zhang
Affiliation:
Hangzhou RoboCT Technology Development Co. Ltd., Hangzhou, China
Yan Shi
Affiliation:
School of Automation Science and Electrical Engineering, Beihang University (BUAA), Beijing, China
Qing Guo*
Affiliation:
School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, China
*
Corresponding Author: Qing Guo; Email: guoqinguestc@uestc.edu.cn
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Abstract

The lower limb exoskeleton is a typical wearable robot designed to assist human motion. However, its system stability and performance are often compromised due to unknown model parameters and inadequate control strategies. Therefore, it is crucial to explore the parametric identification of the exoskeleton and the design of corresponding control strategies for human-exoskeleton cooperative motion. First, an exoskeleton platform is developed to provide experimental validation. Simultaneously, a two-degree-of-freedom (2-DOF) exoskeleton model is constructed using the Lagrange method. The neighborhood field optimization (NFO) technique is then applied to identify the unknown model parameters of the exoskeleton. Additionally, the excitation trajectories for the exoskeleton are developed by the NFO method, incorporating several motion constraints to enhance the accuracy of model identification. An admittance controller is implemented to enable active control of the exoskeleton, allowing it to align with human intention and thereby improving the wearability and comfort of the device. Finally, both simulation and experimental results are compared and verified on the platform. These results demonstrate that the NFO method achieves superior identification accuracy compared to particle swarm optimization (PSO) and genetic algorithm (GA).

Keywords

exoskeletonneighborhood field optimization (NFO)model identificationadmittance control

Information

Type
Research Article
Information
Robotica , First View , pp. 1 - 16
Copyright
© The Author(s), 2025. Published by Cambridge University Press

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