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; 楼旭阳; 马艺飞; 叶倩; 张今旗

doi:10.1631/FITEE.2100586

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输出受限的柔性海洋立管自适应神经网络边界控制

常规文章 | Updated：2022-09-08

- 输出受限的柔性海洋立管自适应神经网络边界控制
- Adaptive neural network based boundary control of a flexible marine riser system with output constraints
- 信息与电子工程前沿（英文） 2022年23卷第8期页码：1229-1238
- Affiliations：
  
  1.Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China
  2.College of Internet of Things Technology, Wuxi Institute of Technology, Wuxi 214121, China
  3.Wuxi Good Cloud IoT Technology Co., Ltd., Wuxi 214196, China
- Author bio：
  
  †E-mail: sunrise_ycy@stu.jiangnan.edu.cn
  ‡Corresponding author
- Funds：
  
  Natural Science Foundation of Jiangsu Province, China(BK20201340);333 High-level Talents Training Project of Jiangsu Province, China, and the Blue Project for Colleges and Universities of Jiangsu Province, China
- DOI：10.1631/FITEE.2100586
  中图分类号： TP13
- 收稿：2021-12-28，
  
  录用：2022-06-12，
  
  纸质出版：2022-08-23
- Accepted：
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余初阳, 楼旭阳, 马艺飞, 等. 输出受限的柔性海洋立管自适应神经网络边界控制[J]. 信息与电子工程前沿（英文）, 2022,23(8):1229-1238.

Chuyang YU, Xuyang LOU, Yifei MA, et al. 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.
余初阳, 楼旭阳, 马艺飞, 等. 输出受限的柔性海洋立管自适应神经网络边界控制[J]. 信息与电子工程前沿（英文）, 2022,23(8):1229-1238. DOI： 10.1631/FITEE.2100586.

Chuyang YU, Xuyang LOU, Yifei MA, et al. 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. DOI： 10.1631/FITEE.2100586.

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。

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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Keywords

references

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