• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

基于非线性鲁棒神经网络反步法的欠驱动船舶航向控制

Course Control of Underactuated Ship Based on Nonlinear Robust Neural Network Backstepping Method.

作者信息

Yuan Junjia, Meng Hao, Zhu Qidan, Zhou Jiajia

机构信息

College of Automation, Harbin Engineering University, Harbin 150001, China.

出版信息

Comput Intell Neurosci. 2016;2016:3013280. doi: 10.1155/2016/3013280. Epub 2016 May 18.

DOI:10.1155/2016/3013280
PMID:27293422
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4887622/
Abstract

The problem of course control for underactuated surface ship is addressed in this paper. Firstly, neural networks are adopted to determine the parameters of the unknown part of ideal virtual backstepping control, even the weight values of neural network are updated by adaptive technique. Then uniform stability for the convergence of course tracking errors has been proven through Lyapunov stability theory. Finally, simulation experiments are carried out to illustrate the effectiveness of proposed control method.

摘要

本文研究了欠驱动水面舰艇的航向控制问题。首先，采用神经网络来确定理想虚拟反步控制未知部分的参数，甚至神经网络的权重值也通过自适应技术进行更新。然后，通过李雅普诺夫稳定性理论证明了航向跟踪误差收敛的一致稳定性。最后，进行了仿真实验以说明所提控制方法的有效性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1760/4887622/4adba1f13b0e/CIN2016-3013280.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1760/4887622/fc84b04fb4ce/CIN2016-3013280.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1760/4887622/a51cb44460bb/CIN2016-3013280.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1760/4887622/cfaee45fc5dc/CIN2016-3013280.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1760/4887622/4adba1f13b0e/CIN2016-3013280.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1760/4887622/fc84b04fb4ce/CIN2016-3013280.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1760/4887622/a51cb44460bb/CIN2016-3013280.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1760/4887622/cfaee45fc5dc/CIN2016-3013280.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1760/4887622/4adba1f13b0e/CIN2016-3013280.004.jpg

相似文献

1
Course Control of Underactuated Ship Based on Nonlinear Robust Neural Network Backstepping Method.基于非线性鲁棒神经网络反步法的欠驱动船舶航向控制
Comput Intell Neurosci. 2016;2016:3013280. doi: 10.1155/2016/3013280. Epub 2016 May 18.
2
Adaptive Robust Output Feedback Control for a Marine Dynamic Positioning System Based on a High-Gain Observer.基于高增益观测器的海洋动力定位系统自适应鲁棒输出反馈控制。
IEEE Trans Neural Netw Learn Syst. 2015 Nov;26(11):2775-86. doi: 10.1109/TNNLS.2015.2396044. Epub 2015 Mar 5.
3
Recurrent fuzzy neural network backstepping control for the prescribed output tracking performance of nonlinear dynamic systems.用于非线性动态系统规定输出跟踪性能的递归模糊神经网络反步控制。
ISA Trans. 2014 Jan;53(1):33-43. doi: 10.1016/j.isatra.2013.08.012. Epub 2013 Sep 20.
4
Adaptive Neural Backstepping Sliding Mode Heading Control for Underactuated Ships with Drift Angle and Ship-Bank Interaction.考虑漂移角和船岸相互作用的欠驱动船舶自适应神经反步滑模航向控制
Comput Intell Neurosci. 2020 Sep 27;2020:8854055. doi: 10.1155/2020/8854055. eCollection 2020.
5
Wavelet adaptive backstepping control for a class of nonlinear systems.一类非线性系统的小波自适应反步控制
IEEE Trans Neural Netw. 2006 Sep;17(5):1175-83. doi: 10.1109/TNN.2006.878122.
6
Robust adaptive neural network control for a class of uncertain MIMO nonlinear systems with input nonlinearities.一类具有输入非线性的不确定多输入多输出非线性系统的鲁棒自适应神经网络控制
IEEE Trans Neural Netw. 2010 May;21(5):796-812. doi: 10.1109/TNN.2010.2042611. Epub 2010 Mar 15.
7
Adaptive neural network decentralized backstepping output-feedback control for nonlinear large-scale systems with time delays.具有时滞的非线性大系统的自适应神经网络分散反步输出反馈控制
IEEE Trans Neural Netw. 2011 Jul;22(7):1073-86. doi: 10.1109/TNN.2011.2146274. Epub 2011 Jun 2.
8
Novel neural control for a class of uncertain pure-feedback systems.一类不确定纯反馈系统的新型神经控制。
IEEE Trans Neural Netw Learn Syst. 2014 Apr;25(4):718-27. doi: 10.1109/TNNLS.2013.2280728.
9
Neural networks-based adaptive control for nonlinear time-varying delays systems with unknown control direction.基于神经网络的未知控制方向非线性时变延迟系统自适应控制
IEEE Trans Neural Netw. 2011 Oct;22(10):1599-612. doi: 10.1109/TNN.2011.2165222. Epub 2011 Aug 30.
10
Adaptive neural control for strict-feedback nonlinear systems without backstepping.无反推的严格反馈非线性系统的自适应神经控制
IEEE Trans Neural Netw. 2009 Jul;20(7):1204-9. doi: 10.1109/TNN.2009.2020982. Epub 2009 May 27.

本文引用的文献

1
Fuzzy Counter Propagation Neural Network Control for a Class of Nonlinear Dynamical Systems.一类非线性动力系统的模糊反向传播神经网络控制
Comput Intell Neurosci. 2015;2015:719620. doi: 10.1155/2015/719620. Epub 2015 Aug 20.
2
Improvement of adaptive GAs and back propagation ANNs performance in condition diagnosis of multiple bearing system using grey relational analysis.基于灰色关联分析的自适应遗传算法和反向传播人工神经网络在多轴承系统状态诊断中的性能改进
Comput Intell Neurosci. 2014;2014:419743. doi: 10.1155/2014/419743. Epub 2014 Dec 18.