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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: 1758

Citations:  Bibtex RefMan EndNote GB/T7714


Xuyang Lou


Chuyang YU


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Frontiers of Information Technology & Electronic Engineering  2022 Vol.23 No.8 P.1229-1238


Adaptive neural network based boundary control of a flexible marine riser system with output constraints

Author(s):  Chuyang YU, Xuyang LOU, Yifei MA, Qian YE, Jinqi ZHANG

Affiliation(s):  Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China; more

Corresponding email(s):   sunrise_ycy@stu.jiangnan.edu.cn, Louxy@jiangnan.edu.cn

Key Words:  Marine riser system, Partial differential equation, Neural network, Output constraint, Boundary control, Unknown disturbance

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.

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%A Jinqi ZHANG
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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
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2100586

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.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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