摘要
本文研究了动态战场环境中的多无人机协同目标分配(MUCTA)问题,首先通过分析UAV分配次序对打击任务总收益的影响,设计了动态战场环境的更新规则,将航程代价和任务代价作为惩罚项修正目标函数,建立了考虑分配次序的UAVs协同目标分配优化模型.然后针对模型的物理意义改进了遗传算法基因编码方式,设计了MUCTA遗传算法,该算法利用状态转移思想,引进SDR算子获得多种分配次序种群,同时以单行变异算子修正UAV与目标对应关系,并采用最优个体法和轮盘赌法筛选子代个体.最后仿真结果验证了所设计算法的有效性.
This article is concerned with the Multi-UA Vs cooperative target assignment (MUCT A) of dynamic battle eld environment. Firstly, By means of the in uence of unmanned aerial vehicle (UA V) allocation order on total revenue of strike task, the updating rules of dynamic battle eld environment are designed. The cost of ight path length and task is used as penalty term in objective function, and the optimization model of UA Vs cooperative target assignment with allocation order is established. Secondly, the coding method of genetic algorithm is improved based on the physical signi cance of the optimization model, and the MUCT A genetic algorithm is proposed. According to state transition, SDR operator is used to obtain different population of various allocation order, single mutation operator is used to adjust the correspondence relation between UA Vs and targets, the methods of optimal individual selection and roulette are used to screen offspring individuals. Finally, simulation results verify the effectiveness of the algorithm.
作者
陈志旺
夏顺
李建雄
王航
王昌蒙
CHEN Zhi-wang;XIA Shun;LI Jian-xiong;WANG Hang;WANG Chang-meng(Key Lab of Industrial Computer Control Engineering of Hebei Province,Yanshan University,Qinhuangdao Hebei 066004,China;National Engineering Research Center for Equipment and Technology of Cold Strip Rolling,Yanshan University,Qinhuangdao Hebei 066004,China)
出处
《控制理论与应用》
EI
CAS
CSCD
北大核心
2019年第7期1072-1082,共11页
Control Theory & Applications
基金
国家自然科学基金项目(61573305)资助~~
关键词
无人机
遗传算法
目标分配
分配模型
unmanned aerial vehicles
genetic algorithms
target assignment
assignment model
