Robust neural network‐based tracking control for unmanned surface vessels under deferred asymmetric constraints

2021 ◽  
Author(s):  
Yanchao Sun ◽  
Chengpeng Li ◽  
Hongde Qin ◽  
Zhongchao Deng ◽  
Zhe Chen
Keyword(s):  
Neural Network ◽  
Tracking Control ◽  
Surface Vessels

Tracking Control of Underactuated Unmanned Surface Vessels Based on the Dynamic Fuzzy Neural Network

2012 ◽  
Vol 562-564 ◽  
pp. 2188-2196
Author(s):  
Yun Yan Hu ◽  
Guo Cheng Zhang ◽  
Lei Wan
Keyword(s):  
Neural Network ◽  
Tracking Control ◽  
Control Algorithm ◽  
Fuzzy Neural Network ◽  
Network Control ◽  
Simulation Experiments ◽  
Surface Vessels ◽  
Fuzzy Neural ◽  
On Line ◽  
Hidden Layer

This paper proposes a dynamic fuzzy neural network (DFNN) for the tracking control of the underactuated unmanned surface vessels. The dynamic fuzzy neural network control algorithm has the advantages of both fuzzy logical and neural network. The algorithm adjusts the structure and parameters on line at the same time to make the tracking effect of the system being fast and accurate, while it doesn’t need confirm the fuzzy rules and the nodes of hidden layer. The simulation experiments based on the proposed control algorithm are carried out and the simulation results validate its effectiveness.

Event-triggered compound learning tracking control of autonomous surface vessels in the measurement network

2021 ◽  
Vol 228 ◽  
pp. 108817
Author(s):  
Yingjie Deng ◽  
Xianku Zhang ◽  
Baigang Zhao ◽  
Hongbiao Zhao
Keyword(s):  
Tracking Control ◽  
Surface Vessels ◽  
Event Triggered
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