Straight-Path Following and Formation Control of USVs Using Distributed Deep Reinforcement Learning and Adaptive Neural Network

被引：1

作者：

Zhengqing Han ^{[1
]}

Yintao Wang ^{[1
]}

Qi Sun ^{[1
]}

机构：

[1] the School of Marine Science and Technology, Northwestern Polytechnical University

来源：

IEEE/CAA Journal of Automatica Sinica | 2023年 / 10卷 / 02期

基金：

中国国家自然科学基金;

关键词：

D O I：

暂无

中图分类号：

V279 [无人驾驶飞机]; TP273 [自动控制、自动控制系统]; TP18 [人工智能理论];

学科分类号：

080201 ; 081104 ; 0812 ; 0835 ; 1111 ; 1405 ;

摘要：

Dear Editor,This letter presents a distributed deep reinforcement learning(DRL) based approach to deal with the path following and formation control problems for underactuated unmanned surface vehicles(USVs). By constructing two independent actor-critic architectures,the deep deterministic policy gradient(DDPG) method is proposed to determine the desired heading and speed command for each USV.

引用

页码：572 / 574

页数：3

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