基于粒子群算法的机动弹头再入弹道优化设计

Reentry Trajectory Design for Maneuverable Warhead Based on Particle Swarm Optimization

在导弹机动突防的研究中,为合理设计与比较分析各种突防方案的优劣,必须首先了解突防弹头的可机动区域,即确定弹头的最小纵程、最大纵程和最大横程。基于粒子群优化算法,采用直接优化控制变量迎角和侧滑角的方法,对再入边界弹道进行了优化设计。为避免陷入局部最小值点、提高群体的多样性,根据再入弹道设计中控制变量的连续性特点,在粒子群优化算法的迭代公式中引入了平滑变异因子。以某机动再入弹头为例,分别得到了满足过程约束及终端状态约束的最大纵程、最小纵程和最大横程弹道。仿真结果表明,改进方法是可行的,可为弹道优化提供参考。

To design and compare various penetration schemes reasonably in the missile maneuver trajectory, we must know the available attack area of the maneuvering warhead, including the minimum longitudinal range, the maximum longitudinal range and the maximum cross range. Based on the Particle Swarm Optimization （PSO） algorithm, using the direct optimal method to compute the attack angle and sideslip angle of control variables, the reentry boundary trajectory is designed. In order to avoid falling into the local minimum point and increase the diversity of the population, considering the continuity of the control variables, the smooth mutation coefficients are introduced into the iterative algorithm. Using a specified maneuvering warhead, we obtain the optimal trajectories that meet process constraints and terminal state constraints. The simulation results show that this method is feasible and has important engineering application value.