基于偏好联盟博弈的导弹集群分布式任务分配模型  

Distributed Task Assignment Model of Missile Swarm Based on Hedonic Coalition Games

作　　者：张贇 邱忠宇 蔡云泽[1] ZHANG Yun;QIU Zhongyu;CAI Yunze(Department of Automation,Shanghai Jiao Tong University,Shanghai,200240;Shanghai Eletro-Mechanical Engineering Institute,Shanghai,201109)

机构地区：[1]上海交通大学自动化系,上海200240 [2]上海机电工程研究所,上海201109

出　　处：《空天防御》2021年第3期24-32,共9页Air & Space Defense

基　　金：国家重大科研仪器专项(61627810);上海航天先进技术联合研究基金(USCAST2020-21);航天先进技术联合研究中心技术创新项目(USCAST2019-2)。

摘　　要：在空空导弹拦截大规模集群的场景中,由于集中式算法求解大规模任务分配问题的性能面临通信能力和实时性的限制,研究使用分布式算法求解任务分配问题具有重要的现实意义。以偏好联盟博弈模型为基础,设计了分布式导弹集群任务分配模型,并使用博弈学习算法中的空间自适应博弈(spatial adaptive play,SAP)算法求解最优分配方案。与集中式算法的仿真对比实验表明,本文设计的基于偏好联盟博弈的分布式任务分配模型可以在分布式框架下求解带约束的任务分配问题,并具有较好的优化性能和收敛效率。In the scenario of air-to-air missiles intercepting large-scale swarms,because the performance of centralized algorithms to solve large-scale task assignment problems is limited by communication capabilities and real-time perfor⁃mance,it is practically important to develop distributed algorithms to solve task assignment problems.Based on the he⁃donic coalition game(HCG)model,this paper designs a distributed missile swarm task assignment model,and uses the spatial adaptive play(SAP)algorithm in the game learning algorithm to achieve the optimal allocation solution.The simulation comparison experiment with the centralized algorithm shows that the distributed task assignment model based on the HCG designed in this paper can solve the distributed task allocation problem with constraints,and has comparable optimization performance and better convergence efficiency.

关 键 词：导弹集群 任务分配 分布式算法 偏好联盟博弈 SAP算法 

分 类 号：V279[航空宇航科学与技术—飞行器设计]