Exoskeleton Follow-Up Control Based on Parameter Optimization of Predictive Algorithm
Author(s) -
Shijia Zha,
Tianyi Li,
Lidan Cheng,
Jihua Gu,
Wei Wei,
Xichuan Lin,
Shaofei Gu
Publication year - 2021
Publication title -
applied bionics and biomechanics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.397
H-Index - 23
eISSN - 1754-2103
pISSN - 1176-2322
DOI - 10.1155/2021/8850348
Subject(s) - exoskeleton , smoothness , position (finance) , control theory (sociology) , signal (programming language) , model predictive control , torque , computer science , algorithm , powered exoskeleton , engineering , control (management) , simulation , artificial intelligence , mathematics , mathematical analysis , physics , finance , economics , thermodynamics , programming language
The prediction of sensor data can help the exoskeleton control system to get the human motion intention and target position in advance, so as to reduce the human-machine interaction force. In this paper, an improved method for the prediction algorithm of exoskeleton sensor data is proposed. Through an algorithm simulation test and two-link simulation experiment, the algorithm improves the prediction accuracy by 14.23 ± 0.5%, and the sensor data is smooth. Input the predicted signal into the two-link model, and use the calculated torque method to verify the prediction accuracy data and smoothness. The simulation results showed that the algorithm can predict the joint angle of the human body and can be used for the follow-up control of the swinging legs of the exoskeleton.
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