Control and Implementation of 2-DOF Lower Limb Exoskeleton Experiment Platform

Chinese Journal of Mechanical Engineering

Huiyu Xiong¹  Zhenlei Chen²  Qing Guo³  Yao Yan³  Dan Jiang⁴ 

[1] Glasgow College, University of Electronic Science and Technology of China, 611731, Sichuan, China;School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, 611731, Sichuan, Chengdu, China;School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, 611731, Sichuan, Chengdu, China;Aircraft Swarm Intelligent Sensing and Cooperative Control Key Laboratory of Sichuan Province, 611731, Sichuan, China;School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, 611731, Sichuan, China;

关键词: Lower limb exoskeleton;    BP neural network;    Backstepping controller;    Variable admittance strategy;   

DOI  :  10.1186/s10033-021-00537-8

来源: Springer

【 摘 要 】

In this study, a humanoid prototype of 2-DOF (degrees of freedom) lower limb exoskeleton is introduced to evaluate the wearable comfortable effect between person and exoskeleton. To improve the detection accuracy of the human-robot interaction torque, a BPNN (backpropagation neural networks) is proposed to estimate this interaction force and to compensate for the measurement error of the 3D-force/torque sensor. Meanwhile, the backstepping controller is designed to realize the exoskeleton's passive position control, which means that the person passively adapts to the exoskeleton. On the other hand, a variable admittance controller is used to implement the exoskeleton's active follow-up control, which means that the person's motion is motivated by his/her intention and the exoskeleton control tries best to improve the human-robot wearable comfortable performance. To improve the wearable comfortable effect, serval regular gait tasks with different admittance parameters and step frequencies are statistically performed to obtain the optimal admittance control parameters. Finally, the BPNN compensation algorithm and two controllers are verified by the experimental exoskeleton prototype with human-robot cooperative motion.

【 授权许可】

CC BY