空海异构无人系统的协同控制与智能优化

柯灿; 陈惠芳; 谢磊

doi:10.11993/j.issn.2096-3920.2024-0173

空海异构无人系统的协同控制与智能优化

doi: 10.11993/j.issn.2096-3920.2024-0173

柯灿¹,
陈惠芳^{1, 2},
谢磊^{1, 2}

1.
浙江大学信息与电子工程学院, 浙江杭州, 310027
2.
浙江省多模态通信网络与智能信息处理重点实验室, 浙江杭州, 310027

基金项目: 受国家重大科研仪器研制项目(42227901), 国家自然科学基金项目(62271442), 浙江省自然科学基金重点项目(LZ23F010006)和浙江省科技厅公益项目(LGG22F010007)资助.

详细信息

作者简介:
柯灿：柯　灿(1995-), 男, 在读博士, 主要研究方向为通信约束下空海异构无人系统的协同控制与智能优化

中图分类号: TJ630.33; U674.941
计量
- 文章访问数: 12
- HTML全文浏览量: 7
- PDF下载量: 3
- 被引次数: 0
出版历程
- 收稿日期: 2024-12-19
- 修回日期: 2025-02-27
- 录用日期: 2025-03-04
- 网络出版日期: 2025-03-24

Cooperative Control and Intelligent Optimization for the Air-sea Heterogeneous Unmanned System

KE Can¹,
CHEN Huifang^{1, 2},
XIE Lei^{1, 2}

1.
College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China
2.
Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing, Hangzhou 310027, China

摘要

摘要: 为应对日益复杂的海洋任务, 文中构建了一个由空中无人机-海面无人艇-水下航行器组成的空海异构无人系统, 研究其协同控制问题。对于异构无人系统的信息交互问题, 各域均由一个领航者与多个跟随者组成, 跨域通信由各域领航者完成。同时, 针对各域领航者轨迹问题, 提出了一种基于人工势场法的协同轨迹规划算法, 使各域领航者避障下到达目标位置。对于受限的通信资源问题, 为各域跟随者设计了一种基于间歇通信的脉冲层级编队控制协议, 实现了避障下的编队控制, 且减少了通信开销。另外, 针对协同控制算法的收敛时间与通信能耗的多目标优化问题, 通过设计收缩-扩张系数和动态密集距离策略, 提出了一种改进的多目标量子行为粒子群优化算法, 用于智能选择各域脉冲间隔, 从而协同控制算法的收敛时间与通信能耗间达到良好折衷。仿真结果表明, 空海异构无人系统能够在避障下实现编队控制, 同时减少通信开销, 与传统的多目标量子行为粒子群优化算法相比, 所提智能优化算法具有更好的收敛性与全局搜索能力。
- 空海异构无人系统 /
- 通信约束 /
- 协同控制 /
- 智能优化
Abstract: In order to cope with increasingly complex ocean missions, an air-sea heterogeneous unmanned system composed of unmanned aerial vehicle(UAV)-unmanned surface vehicle(USV)-unmanned underwater vehicle(UUV) is constructed to study the cooperative control problem in this paper. For the information exchange problem of heterogeneous unmanned system, each domain consists of a leader and multiple followers, where the cross-domain communication is required between the leaders of each domain. Meanwhile, for the-0.6 trajectory planning issue of each domain leader, a cooperative trajectory planning algorithm based on the artificial potential field method is proposed for each domain's leader to reach the target location while avoiding obstacles. For the limited communication resources problem, an impulsive hierarchical formation control protocol with intermittent communication is designed for followers in each domain, which reduces communication costs while achieving formation control under obstacle avoidance. Besides, for the multi-objective optimization problem of convergence time and communication energy consumption in cooperative control algorithm, an improved multi-objective quantum particle swarm optimization algorithm is proposed by designing contraction-expansion coefficient and dynamic dense distance strategy, which is used to intelligently select the optimal impulsive interval for each domain, achieving a good compromise between the convergence time and communication energy consumption of cooperative control algorithm. Simulation results demonstrate that the air-sea heterogeneous unmanned system can achieve formation control while avoiding obstacles and reducing communication overhead, and the proposed intelligent optimized algorithm has better convergence and global search ability than the traditional multi-objective quantum-behavior particle swarm optimization algorithm.
- air-sea heterogeneous unmanned system /
- communication constraints /
- cooperative control /
- intelligent optimization

HTML全文

图 1 空海异构无人系统

Figure 1. Air-sea heterogeneous unmanned system

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图 2 MOIQPSO算法流程图

Figure 2. Flow chart of MOIQPSO algorithm

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图 3 空海异构无人系统的网络拓扑

Figure 3. The network topology of air-sea heterogeneous unmanned system

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图 4 空海异构无人系统的轨迹与X-Y平面的编队

Figure 4. Trajectories and X-Y formation of air-sea heterogeneous unmanned system

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图 5 领航者间的通信距离

Figure 5. Communication range between leaders

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图 6 X轴位置误差

Figure 6. Position X error

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图 7 Y轴位置误差

Figure 7. Position Y error

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图 8 Z轴位置误差

Figure 8. Position Z error

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图 9 通信能耗

Figure 9. Communication energy consumption

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图 10 通信能耗总和

Figure 10. Total communication energy consumption

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图 11 编队跟踪误差总和

Figure 11. Total formation tracking error

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图 12 Pareto解的分布情况

Figure 12. Distribution of Pareto solutions

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表 1 不同算法的性能比较

Table 1. Comparison of performance among different algorithms.

外部解

算法 $ {e_{{\mathrm{nf}}}} $ $ {\tilde W_{{\mathrm{total}}}} $ S $ I_{\mathrm{HV}} $

MOQPSO 0.1843 0.1481 0.033 16.05
MOIQPSO 0.1574 0.1351 0.023 18.81

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