Full Text:   <198>

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Suppl. Mater.: 

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: 167

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Xuyang Lou

https://orcid.org/0000-0002-7499-1308

Chuyang YU

https://orcid.org/0000-0002-2828-8355

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

http://doi.org/10.1631/FITEE.2100586


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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volume="23",
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publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2100586"
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%A Yifei MA
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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
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A1 - Qian YE
A1 - Jinqi ZHANG
J0 - Frontiers of Information Technology & Electronic Engineering
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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.

输出受限的柔性海洋立管自适应神经网络边界控制

余初阳1,楼旭阳1,马艺飞1,叶倩2,张今旗3
1江南大学轻工业先进过程控制教育部重点实验室,中国无锡市,214122
2无锡职业技术学院物联网技术学院,中国无锡市,214121
3无锡佳云丰物联网科技有限公司,中国无锡市,214196
摘要:针对具有未知非线性扰动和输出限制的柔性海洋立管系统,提出一种基于自适应神经网络的边界控制方法抑制振动。首先,通过偏微分方程分布参数系统描述柔性海洋立管系统的动态特性。为补偿非线性扰动对系统影响,利用径向基神经网络构造一个基于神经网络的边界控制器以减少振动。在所提边界控制器下,基于李亚普诺夫方法,保证柔性海洋立管系统一致有界。该方法为其他柔性机器人系统的边界控制提供了一种集成神经网络的思路。最后,通过数值仿真验证所提方法的有效性。

关键词:海洋立管系统;偏微分方程;神经网络;输出限制;边界控制;未知扰动

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

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