系统工程与电子技术 ›› 2025, Vol. 47 ›› Issue (2): 591-599.doi: 10.12305/j.issn.1001-506X.2025.02.25

• 制导、导航与控制 • 上一篇    

弱通信下无人潜航器事件触发一致性协同控制

赵万兵, 夏元清, 戴荔, 张元   

  1. 北京理工大学自动化学院, 北京 100081
  • 收稿日期:2024-03-27 出版日期:2025-02-25 发布日期:2025-03-18
  • 通讯作者: 夏元清
  • 作者简介:赵万兵 (1993—), 男, 副教授, 博士, 主要研究方向为多智能体智能协同控制、事件驱动控制、鲁棒容错控制
    夏元清 (1971—), 男, 教授, 博士, 主要研究方向为无人移动平台协同控制、空天地海一体化网络环境下多运动体系统跨越协同控制与智能决策、云控制与决策
    戴荔 (1988—), 女, 教授, 博士, 主要研究方向为多智能体控制理论与应用、模型预测控制理论及应用
    张元 (1993—), 男, 副教授, 博士, 主要研究方向为网络化系统分析与优化、数据驱动理论及其应用
  • 基金资助:
    国家自然科学基金(62303050);中国博士后科学基金(2023M730254);中国博士后科学基金(2024T171122);国家资助博士后研究人员计划(GZB20230936)

Event-triggered consensus cooperative control of unmanned underwater vehicle under adverse communication condition

Wanbing ZHAO, Yuanqing XIA, Li DAI, Yuan ZHANG   

  1. School of Automation, Beijing Institute of Technology, Beijing 100081, China
  • Received:2024-03-27 Online:2025-02-25 Published:2025-03-18
  • Contact: Yuanqing XIA

摘要:

针对水下无人潜航器(unmanned underwater vehicle, UUV)集群在弱通信条件下的一致性协同控制问题, 考虑水下群间通信存在的高延时、低带宽、需具有隐蔽性等弱通信特点, 设计基于强化学习的事件触发智能一致性协同控制架构, 以实现UUV集群在弱通信条件下的有效协同。首先, 设计一个事件触发分布式观测器, 该观测器利用领导者与邻居的动态交互信息, 来估计弱通信条件下UUV所需的跟踪参考信号。随后, 采用强化学习方法直接从系统交互中学习最优控制策略。最后, 通过仿真结果验证了所提方法的有效性。

关键词: 弱通信, 事件触发, 一致性协同控制, 强化学习

Abstract:

Aiming at the problem of consensus collaborative control of unmanned underwater vehicle (UUV) clusters under adverse communication conditions, considering the adverse communication characteristics of high latency, low bandwidth, and the need for stealth in underwater inter-cluster communication, a reinforcement learning based event-triggered intelligent consensus collaborative control architecture is designed to achieve effective collaboration of UUV clusters under adverse communication conditions. Firstly, an event-triggered distributed observer is devised which utilizes dynamic interaction information between leaders and neighbors to estimate the tracking reference signal required for UUVs under adverse communication conditions. Subsequently, reinforcement learning methods are used to directly learn the optimal control strategy from system interactions. Finally, the effectiveness of the proposed method is validated through simulation results.

Key words: adverse communication condition, event-triggered, consensus cooperative control, reinforce-ment learning

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