航空学报 > 2025, Vol. 46 Issue (15): 331480-331480   doi: 10.7527/S1000-6893.2024.31480

面向空中战斗管理的协同任务进程管理方法

宋祺, 左家亮(), 吴傲, 杨任农, 王瑛, 李乐言   

  1. 空军工程大学 空管领航学院,西安 710051
  • 收稿日期:2024-11-01 修回日期:2024-12-02 接受日期:2024-12-24 出版日期:2025-02-06 发布日期:2025-01-07
  • 通讯作者: 左家亮 E-mail:hudyuan@163.com
  • 基金资助:
    国家级项目

Collaborative mission schedule management method for air battle management

Qi SONG, Jialiang ZUO(), Ao WU, Rennong YANG, Ying WANG, Leyan LI   

  1. Air Traffic Control and Navigation College,Air Force Engineering University,Xi’an 710051,China,
  • Received:2024-11-01 Revised:2024-12-02 Accepted:2024-12-24 Online:2025-02-06 Published:2025-01-07
  • Contact: Jialiang ZUO E-mail:hudyuan@163.com

摘要:

针对大规模空中作战易出现“枪炮一响,计划泡汤”的难题,提出了一种协同任务进程管理方法。首先,引入管理学WBS工作分解结构,将作战整体任务分解为编队行为;其次,围绕计划制定,提出了一种带时间线的双代号群体网络的进程计划表征模型,给出了计划的机器求解算法和人工计划的冲突检测与消解算法;在此基础上,瞄准任务结果与执行过程,建立多目标优化模型,使用NSGA-Ⅱ算法求解帕累托最优计划;然后,基于闭环反馈思想建立了计划实时控制系统,运用带约束的自适应差分进化算法求解控制策略;最后,利用“墨子”推演系统的公开作战想定进行实验验证,共设置无扰动、有扰动无控制、有扰动有控制、扰动超出最大控制范围4个实验。实验结果表明,提出的任务进程管理方法,能够生成无冲突、满足约束的任务进程计划,并且能在最大抗扰动范围内对任务进程进行精确控制,确保任务的顺利完成。

关键词: 空中战斗管理, 协同任务进程管理, 双代号群体网络, 多目标优化, 自适应差分进化算法, 抗扰动控制

Abstract:

To address the challenge of“ plans collapsing with the first shot fired” in large-scale air operations, a collaborative mission schedule management approach is proposed. Initially, the Work Breakdown Structure (WBS) from management science is introduced to decompose the overall combat mission into formation behaviors. Subsequently,focusing on plan formulation, a timeline-based activity on arrow group network representation model for schedule planning is introduced, along with machine-solving algorithms for plans and conflict detection and resolution algorithms for manually crafted plans. Building on this foundation, a multi-objective optimization model targeting mission outcomes and execution processes is established, and the NSGA-Ⅱ algorithm is used to obtain Pareto-optimal plans. Further more, a real-time plan control system is constructed based on closed-loop feedback principles, utilizing a constrained adaptive differential evolution algorithm to derive control strategies. Finally, experimental validation is conducted using publicly available combat scenarios in the“ Mozi” simulation system, with four scenarios set up: no disturbance, disturbance without control, disturbance with control, and disturbance exceeding maximum control range. The experimental results demonstrate that the proposed mission progress management method can generate conflict-free,constraint-satisfying mission schedule plans and enable precise control of mission schedule within the maximum disturbance resistance range, ensuring the successful completion of missions.

Key words: air battle management, collaborative mission schedule management, group network of activity on arrow, multi-objective optimization, adaptive differential evolution algorithm, disturbance resistance control

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