摘要
任务分配是多无人作战飞机(UCAV)协同控制的基础.对此,分析了影响任务分配的关键战技指标,建立了针对攻击任务的多UCAV协同任务分配模型.应用连续粒子群算法对问题进行求解,建立了粒子与实际问题间的映射,通过位置饱和策略构造粒子的搜索空间,采用自适应惯性权重提高粒子群算法的收敛速度和全局寻优能力.考虑到单机的任务载荷限制,引入了买卖合同机制以实现多机任务协调.仿真结果表明,所提出模型和算法可以较好地解决多UCAV协同任务分配问题.
Task assignment is one of fundamental problems in multiple unmanned combat aerial vehicle（UCAV） cooperative control. Therefore, the factors which effect the task assignment are analyzed, the multiple UCAV cooperative task assignment model for attacking the ground targets is built. The particle swarm optimization（PSO） algorithm for solving such a problem is proposed, based on proper task assignment solution to PSO particle mapping. In order to reduce the search space, a saturation strategy is provided. An adaptive inertia weight strategy is also introduced into the algorithm to balance the global and the local search ability. Considering the capacitated limitation of UCAV, the buy-sell contract scheme is adopted to solve task coordination. The simulation results show that the model and the algorithm can effectively solve the task assignment problems for multiple UCAV.
出处
《控制与决策》
EI
CSCD
北大核心
2012年第11期1751-1755,共5页
Control and Decision
基金
国家自然科学基金项目(60304004,71171199)
关键词
无人作战飞机
协同任务分配
粒子群算法
自适应惯性权重
买卖合同
unmanned combat aerial vehicle
cooperative task assignment
particle swarm optimization
adaptive inertiaweight
buy-sell contract
