CLC number: TP242
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2020-04-07
Cited: 0
Clicked: 6205
Citations: Bibtex RefMan EndNote GB/T7714
Tao Xue, Zi-wei Wang, Tao Zhang, Ou Bai, Meng Zhang, Bin Han. Fixed-time constrained acceleration reconstruction scheme for robotic exoskeleton via neural networks[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(5): 705-722.
@article{title="Fixed-time constrained acceleration reconstruction scheme for robotic exoskeleton via neural networks",
author="Tao Xue, Zi-wei Wang, Tao Zhang, Ou Bai, Meng Zhang, Bin Han",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="21",
number="5",
pages="705-722",
year="2020",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1900418"
}
%0 Journal Article
%T Fixed-time constrained acceleration reconstruction scheme for robotic exoskeleton via neural networks
%A Tao Xue
%A Zi-wei Wang
%A Tao Zhang
%A Ou Bai
%A Meng Zhang
%A Bin Han
%J Frontiers of Information Technology & Electronic Engineering
%V 21
%N 5
%P 705-722
%@ 2095-9184
%D 2020
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1900418
TY - JOUR
T1 - Fixed-time constrained acceleration reconstruction scheme for robotic exoskeleton via neural networks
A1 - Tao Xue
A1 - Zi-wei Wang
A1 - Tao Zhang
A1 - Ou Bai
A1 - Meng Zhang
A1 - Bin Han
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 21
IS - 5
SP - 705
EP - 722
%@ 2095-9184
Y1 - 2020
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1900418
Abstract: Accurate acceleration acquisition is a critical issue in the robotic exoskeleton system, but it is difficult to directly obtain the acceleration via the existing sensing systems. The existing algorithm-based acceleration acquisition methods put more attention on finite-time convergence and disturbance suppression but ignore the error constraint and initial state irrelevant techniques. To this end, a novel radical bias function neural network (RBFNN) based fixed-time reconstruction scheme with error constraints is designed to realize high-performance acceleration estimation. In this scheme, a novel exponential-type barrier Lyapunov function is proposed to handle the error constraints. It also provides a unified and concise Lyapunov stability-proof template for constrained and non-constrained systems. Moreover, a fractional power sliding mode control law is designed to realize fixed-time convergence, where the convergence time is irrelevant to initial states or external disturbance, and depends only on the chosen parameters. To further enhance observer robustness, an RBFNN with the adaptive weight matrix is proposed to approximate and attenuate the completely unknown disturbances. Numerical simulation and human subject experimental results validate the unique properties and practical robustness.
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