CLC number: TP13
On-line Access: 2022-08-22
Received: 2021-12-28
Revision Accepted: 2022-08-29
Crosschecked: 2022-06-12
Cited: 0
Clicked: 2259
Citations: Bibtex RefMan EndNote GB/T7714
Chuyang YU, Xuyang LOU, Yifei MA, Qian YE, Jinqi ZHANG. Adaptive neural network based boundary control of a flexible marine riser system with output constraints[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(8): 1229-1238.
@article{title="Adaptive neural network based boundary control of a flexible marine riser system with output constraints",
author="Chuyang YU, Xuyang LOU, Yifei MA, Qian YE, Jinqi ZHANG",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="23",
number="8",
pages="1229-1238",
year="2022",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2100586"
}
%0 Journal Article
%T Adaptive neural network based boundary control of a flexible marine riser system with output constraints
%A Chuyang YU
%A Xuyang LOU
%A Yifei MA
%A Qian YE
%A Jinqi ZHANG
%J Frontiers of Information Technology & Electronic Engineering
%V 23
%N 8
%P 1229-1238
%@ 2095-9184
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2100586
TY - JOUR
T1 - Adaptive neural network based boundary control of a flexible marine riser system with output constraints
A1 - Chuyang YU
A1 - Xuyang LOU
A1 - Yifei MA
A1 - Qian YE
A1 - Jinqi ZHANG
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
IS - 8
SP - 1229
EP - 1238
%@ 2095-9184
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2100586
Abstract: In this study, we develop an adaptive neural network based boundary control method for a flexible marine riser system with unknown nonlinear disturbances and output constraints to suppress vibrations. We begin with describing the dynamic behavior of the riser system using a distributed parameter system with partial differential equations. To compensate for the effect of nonlinear disturbances, we construct a neural network based boundary controller using a radial basis neural network to reduce vibrations. Under the proposed boundary controller, the state of the riser is guaranteed to be uniformly bounded based on the Lyapunov method. The proposed methodology provides a way to integrate neural networks into boundary control for other flexible robotic manipulator systems. Finally, numerical simulations are given to demonstrate the effectiveness of the proposed control method.
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