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基于梯度搜索的高效性和粒子群搜索的随机性,提出了一种混合粒子群算法,并应用该算法研究了运载火箭上升段交会弹道快速优化设计问题.以运载火箭与目标飞行器在交会时刻的距离最小为目标函数,设计了运载火箭飞行程序,建立了运载火箭上升段交会弹道优化模型,同时分别采用混合粒子群算法、遗传算法和粒子群算法进行求解.仿真结果表明:基于本文算法对运载火箭上升段交会弹道进行优化设计,平均交会位置误差为4.137m,较遗传算法减少了17.940m,平均优化耗时488.922s,较粒子群算法缩短了2 342.125s.混合粒子群算法搜索速度较快,收敛精度较高,可用于运载火箭上升段交会弹道的快速优化设计.
Based on the efficiency of gradient search and the randomness of particle swarm search, a hybrid particle swarm optimization algorithm is proposed and used to study the problem of rapid optimization of cross-meeting ballistic trajectory of ascending rocket in launch vehicle. And the minimum distance as the objective function, the rocket flight program was designed and the ballistic optimization model of the rocket ascending stage intersections was established. At the same time, the hybrid particle swarm optimization algorithm, the genetic algorithm and the particle swarm optimization algorithm were used respectively to solve the problem. Simulation results show that, The rocket rise section intersection trajectory is optimized, the average position error of intersection is 4.137m, which is 17.940m less than the genetic algorithm, the average optimization time is 488.922s, which is 2 342.125s shorter than the particle swarm optimization algorithm. Hybrid particle swarm optimization algorithm searches faster , Which has higher convergence precision and can be used to optimize the trajectory of rendezvous trajectory at launch stage.