多无人艇集群协同控制研究进展与未来趋势

彭周华; 吴文涛; 王丹; 刘陆

doi:10.19693/j.issn.1673-3185.01923

多无人艇集群协同控制研究进展与未来趋势

doi: 10.19693/j.issn.1673-3185.01923

大连海事大学船舶电气工程学院，辽宁大连 116026

基金项目: 国家自然科学基金资助项目（61673081, 51979020, 51909021, 51939001）；水下机器人重点实验室稳定支持基金资助项目（JCKYS2019604SXJQR-01）

详细信息

作者简介:
彭周华，男，1982年生，博士，教授，博士生导师。研究方向：海洋航行器制导与控制，无人船集群控制。 E-mail：zhpeng@dlmu.edu.cn

吴文涛，男，1995年生，硕士生。研究方向：无人船集群运动控制。E-mail：wuwentaodlmu@gmail.com

王丹，男，1960年生，博士，教授，博士生导师。研究方向：智能控制理论，无人船集群控制，电力电子技术。E-mail：dwang@dlmu.edu.cn

刘陆，1990年生，博士，副教授，硕士生导师。研究方向：多无人船集群控制。Email：luliu@dlmu.edu.cn

通信作者:
刘陆

中图分类号: U674.91
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- 被引次数: 0
出版历程
- 收稿日期: 2020-04-10
- 修回日期: 2020-08-01
- 网络出版日期: 2021-01-15
- 刊出日期: 2021-02-28

Coordinated control of multiple unmanned surface vehicles: recent advances and future trends

Marine Electrical Engineering College, Dalian Maritime University, Dalian 116026, China

多无人艇集群协同控制研究进展与未来趋势由彭周华，等创作，采用知识共享署名4.0国际许可协议进行许可。

摘要

摘要: 当前，海洋航行器呈现智能化、网络化、集群化等重要发展趋势，多无人艇通过协同实现集群化作业，是未来海洋作业的主要形式之一。从无人艇运动数学模型出发，分析了多无人艇集群控制所面临的问题和挑战，根据多无人艇运动不同的场景，从轨迹导引、路径导引、目标导引3个方面综述了多无人艇集群协同控制的研究进展。最后，对多无人艇协同控制的研究方向和未来趋势进行了总结和展望。
- 多无人艇 /
- 编队控制 /
- 协同控制 /
- 集群控制
Abstract: The future trends of marine operations will be intelligent, networked and swarmed marine vehicles. One of the main trends of future marine operations will be joint operations through the coordination of multiple unmanned surface vehicles (USVs). As an introduction to the mathematical model of USVs, this paper presents the key problems and fundamental challenges of motion controlling multiple USVs. Regarding different mission scenarios of USVs, the existing results will be classified into trajectory-guided, path-guided, and target-guided coordinated control. In conclusion, we discussed and summarized the future trends of the coordinated control of multiple USVs.
- multiple autonomous surface vehicles /
- formation control /
- coordinated control /
- flocking control

HTML全文

图 1 地球坐标系和艇体坐标系

Figure 1. Reference frames: earth-fixed reference frame and body-fixed reference frame

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图 2 多无人艇协同控制器结构

Figure 2. Coordinated control architectures of multiple unmanned surface vehicles

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图 3 基于轨迹导引的集群控制

Figure 3. Trajectory-guided coordinated control

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图 4 基于路径导引的集群控制

Figure 4. Path-guided coordinated control

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图 5 基于目标导引的集群控制

Figure 5. Target-guided coordinated control

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表 1 轨迹导引、路径导引和目标导引的集群协同控制结构与优缺点

Table 1. Coordinated control architectures, their advantages and disadvantages of TRCC, PACC and TACC

控制结构	轨迹导引	路径导引	目标导引	优点	缺点
集中式控制	[34, 51]	[11, 22, 35, 47, 77, 85-92]	−	统一决策，全局最优	全局通信，带宽要求高，扩展性差，容错性低
分散式控制	−	−	[14, 27, 41, 52, 53, 56, 75, 79, 82, 84, 93-98]	扩展性强，局部信息模块化	局部最优，无信息交互
分布式控制	[21, 38, 42, 44, 71, 74, 99-102]	[17, 28, 45, 46, 48, 80, 103]	[104- 105]	扩展性强，容错性高，信息交互，带宽要求低	拓扑复杂，通信依赖

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表 2 轨迹导引、路径导引和目标导引的集群协同控制器设计方法

Table 2. Design methods of coordinated controller for TRCC, PACC, TACC

设计方法	轨迹导引	路径导引	目标导引
反步法	[34, 51]	[10, 22, 87, 106]	[75, 79, 98, 104]
动态面	[42, 44, 71, 74, 100]	[11, 64, 80, 91]	[41, 56]
跟踪微分器	−	[45-48, 90, 103]	[107]
指令调节器	−	[17, 28]	−

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表 3 轨迹导引、路径导引和目标导引的控制方法

Table 3. Control methods of TRCC, PACC, TACC

控制方法	轨迹导引	路径导引	目标导引
滑模控制	[21]	−	[20]
参数自适应	−	[22]	[41]
神经网络	[42, 44, 71, 100]	[28, 45, 64, 80, 90- 91]	[41, 56, 75, 94, 98]
模糊逻辑	−	[17]	[107]
抗扰控制	[34, 51]	[10, 11, 46, 47, 103]	[9, 56, 105]
约束控制	[38]	[17]	[56, 79, 94, 107]
输出反馈	[38, 44, 71]	[28, 46, 103]	−
避障避碰	[34, 51, 100]	[46, 80, 103]	[75, 98]
连通保持	[100]	[46, 80, 103]	[75]

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