深海无人装备控制系统研究现状与发展趋势

王彪; 罗瑞龙; 王芳; 崔维成

doi:10.11993/j.issn.2096-3920.2025-0074

深海无人装备控制系统研究现状与发展趋势

doi: 10.11993/j.issn.2096-3920.2025-0074

1.
上海海洋大学工程学院, 上海, 201306
2.
西湖大学工学院, 浙江杭州, 310030

详细信息

中图分类号: P715.5; TP242
计量
- 文章访问数: 22
- HTML全文浏览量: 7
- PDF下载量: 4
- 被引次数: 0
出版历程
- 收稿日期: 2025-05-29
- 修回日期: 2025-06-07
- 录用日期: 2025-06-09
- 网络出版日期: 2025-06-11

Research Status and Development Trends of Deep-sea Unmanned Equipment Control System

1.
College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China
2.
School of Engineering, Westlake University, Hangzhou 310030, China

摘要

摘要: 深海无人装备作为国家海洋科技实力的战略体现, 已广泛应用于资源探测、海洋科学研究、军事安全及经济开发等核心领域。其控制系统作为实现复杂水下作业的神经中枢, 直接决定装备的任务执行效能。文章系统梳理了深海无人装备的控制理论体系, 包括传统比例-积分-微分控制、基于模型的控制、数据驱动的智能控制及多智能体控制等技术路径, 深入剖析了集中式、分层式、分布式及混合式控制架构的技术特性与工程适用性。通过对比分析导航定位、通信传输和能源供给等关键技术的研究现状, 揭示了模型不确定性、鲁棒控制性能、多装备协同机制等行业共性挑战。研究表明, 未来控制系统将朝着人工智能深度赋能、集群化协同作业、新型通信与能源技术融合以及跨学科融合的方向发展, 为深海装备智能化转型提供理论与技术支撑。
- 深海无人装备 /
- 控制理论 /
- 控制架构 /
- 关键技术
Abstract: Deep-sea unmanned equipment, as a strategic reflection of a nation's marine scientific and technological strength, has been widely integrated into core fields such as resource exploration, marine scientific research, military security, and economic development. The control system, serving as the neural center for complex underwater operations, directly determines the mission execution efficiency of the equipment. This paper systematically combs the control theory system of deep-sea unmanned equipment, including technical paths such as traditional PID control, model-based robust control, data-driven intelligent control, and multi-agent collaborative control. It deeply analyzes the technical characteristics and engineering applicability of centralized, hierarchical, distributed, and hybrid control architectures. By comparing and analyzing the research status of key technologies such as navigation and positioning, communication transmission, and energy supply, the paper reveals common challenges in the industry, including model uncertainty, robust control performance, and multi-equipment collaboration mechanisms. The study shows that future control systems will develop towards deep empowerment of artificial intelligence, clustered collaborative operations, integration of new communication and energy technologies, and interdisciplinary innovation, providing theoretical and technical support for the intelligent transformation of deep-sea equipment.
- deep-sea unmanned equipment /
- control theory /
- control architecture /
- key technology

HTML全文

图 1 观察级深海无人装备集中式控制架构

Figure 1. Centralized control architecture of observation- class unmanned underwater equipment

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图 2 AUV三层式控制架构

Figure 2. Three-layer control architecture of AUVs

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图 3 深海无人装备分布式控制架构

Figure 3. Distributed control architecture of unmanned underwater equipment

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图 4 水下导航定位原理框图

Figure 4. Schematic diagram of underwater navigation and positioning

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图 5 水下光通信试验平台

Figure 5. Test-bed for underwater optical communication

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表 1 不同控制理论特点对比

Table 1. Characteristics comparison of different control theories

类别	控制方法	优势	劣势	典型应用
传统控制	PID控制	简单稳定, 易于工程实现	无法处理非线性、时变或强扰动环境	底层运动控制
	自抗扰控制	抗强扰动, 鲁棒性	ESO增加计算量, 对测量噪声敏感	底层运动控制
	滑模控制	结构简单, 强鲁棒性	存在抖振	悬停控制、轨迹跟踪
	模型预测控制	显式处理约束, 动态优化能力强	计算复杂度高, 实时性受限	轨迹跟踪、避障
	自适应控制	动态适应环境变化, 鲁棒性较强	参数调整算法复杂, 暂态不稳	动态调参
智能控制	模糊控制	无需精确模型, 适合非线性系统	需先验知识, 高维系统调试困难	轨迹跟踪, 避障
	深度学习	端到端优化, 数据驱动	需大量标注数据, 可解释性差	图像增强、目标识别
	强化学习	无需环境模型, 支持多目标优化	训练效率低, 需大量交互数据	路径规划、编组控制

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表 2 不同动力电池性能对比

Table 2. Performance comparison of different power batteries

电池类型	质量能量密度/(Wh/kg)	体积能量密度/(Wh/L)	循环寿命 /次	特点	代表性装备(国别)
铅酸电池	25~45	40~80	300	稳定, 低成本, 有气体析出	Nautile(法)
银锌电池	80~110	180~200	30	稳定, 大电流放电, 高成本	Shinkai 6500(日)
锂离子电池	120~270	320~750	>500	相对稳定, 高能量密度	“思源号”(中)
固态电池	220~550	450~1200	>1000	高能量密度, 技术尚未成熟	“探索一号”(中)
燃料电池	500~700	1000~1200		需解决深海燃料存储问题	Hugin(挪)

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