High dynamic cooperative topology online optimization and distributed guidance method
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摘要:
针对高速飞行器集群飞行过程中的分布式协同制导问题，提出了基于最小生成树的自适应拓扑优化与制导方法。构建了飞行器集群通信链路的拓扑图，根据通信代价将距离量化为权值，通过最小生成树中Kruskal算法思想实时生成最优通信拓扑图；采用分布式双层协同制导构架与多中心分布式拓扑优化决策方法，在一致性协同控制律中对协调变量进行补偿计算；实现了飞行器集群协同飞行中的拓扑自适应优化调控。仿真验证结果表明了所提方法的有效性与优异性能。
Abstract:In order to solve the distributed cooperative guidance problem in the flight process of a high-velocity aircraft swarm, this paper proposed an adaptive topology optimization and guidance algorithm based on a minimum spanning tree. The algorithm constructed the topology graph of the communication link of the aircraft swarm, quantified the distance as the weight according to the communication cost, and generated the optimal communication topology graph in real time through the idea of the Kruskal algorithm in the minimum spanning tree. This paper adopted the distributed two-layer cooperative guidance framework and multi-center distributed topology optimization decision-making method and compensated for the consistent variables in the consensus cooperative control law, so as to realize the topology adaptive optimization and control in the cooperative flight of aircraft swarm. Simulation results verify the effectiveness and excellent performance of the proposed algorithm.
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图 1 协同飞行自适应拓扑优化结构流程
Figure 1. Flow chart of adaptive topology optimization structure for cooperative flight
图 2 通信链路拓扑构建示意图
Figure 2. Schematic diagram of communication link topology construction
图 4 协同飞行拓扑优化算法示意图
Figure 4. Schematic diagram of cooperative flight topology optimization algorithm
图 6 3个中心协同飞行过程拓扑结构变化
Figure 6. Topological structure changes during cooperative flight of three center
表 1 仿真条件设置
Table 1. Simulation condition setting
飞行器 初始纬度/（°） 初始经度/（°） 终端方位角/（°） 机动幅值 1 10.01 99.98 −95 0.1 2 10.00 99.98 −94 0.2 3 9.99 99.98 −93 0.3 4 10.01 99.99 −92 0.4 5 10.00 99.99 −91 0.5 6 9.99 99.99 −90 0.1 7 10.01 100.00 −89 0.2 8 10.00 100.00 −88 0.3 9 9.99 100.00 −87 0.4 10 10.00 100.01 −86 0.5 
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