摘要
为了解决复杂、多约束条件下的导弹敏捷转弯弹道优化问题,提出了基于Hammersley序列的天牛须-粒子群算法(H-BSO算法)。通过对传统导弹动力学模型进行改进,建立了减速调姿装置安装在尾部的敏捷导弹俯仰通道动力学模型,给出了导弹敏捷转弯弹道优化问题的数学描述。算法通过引入Hammersley序列完成种群初始化,获得更均匀的初始种群分布;加入非线性调节因子以实现动态改变搜索步长,从而提升算法的全局寻优能力。为了验证算法的有效性,给出了不同条件下的仿真实验。仿真结果表明,H-BSO算法在迭代精度、收敛速度及稳定性方面均有显著提升,可有效解决导弹敏捷转弯弹道离线优化问题。
In order to solve the missile agile turning trajectory optimization problem under complex and multi-constrained conditions,the Beetle Swarm Optimization algorithm based on the Hammersley sequence(H-BSO algorithm)is proposed.By improving the traditional missile dynamics model,a dynamic model of the pitch channel of the agile missile is established,which the deceleration and attitude adjustment device is installed at the tail.The mathematical description of the optimization of the missile agile turning trajectory is given.The algorithm completes the population initialization by introducing the Hammersley sequence,so as to obtain a more uniform initial population distribution.The nonlinear adjustment factor is added to dynamically change the search step size,thereby improving the global optimization ability of the algorithm.In order to verify the effectiveness of the algorithm,simulation experiments under different conditions are given.The simulation results show that the H-BSO algorithm has a significant improvement in iterative accuracy,convergence speed and stability,and can effectively solve the offline optimization problem of missile agile turning trajectory.
作者
李梓源
于剑桥
李佳讯
Li Ziyuan;Yu Jianqiao;Li Jiaxun(School of Aerospace Engineering,Beijing Institute of Technology,Beijing 100081,China)
出处
《战术导弹技术》
北大核心
2023年第3期32-41,共10页
Tactical Missile Technology
关键词
低差异序列
粒子群算法
天牛须搜索算法
非线性调节因子
全局寻优
敏捷转弯
弹道优化
low discrepancy sequence
particle swarm algorithm
beetle antennae search algorithm
nonlinear adjustment factor
global optimization
agile turn
trajectory optimization
