基于混合粒子群优化的动力飞行器再入轨迹优化  

Trajectory Optimization of Powered Vehicle Based on Mixed Particle Swarm Optimization Algorithm

作　　者：施健峰[1] 刘运鹏[1] 王宇航[1] Shi Jianfeng Liu Yunpeng Wang Yuhang(Beijing Aerospace Automatic Control Institute, Beijing 100854, China)

机构地区：[1]北京航天自动控制研究所,北京100854

出　　处：《战术导弹技术》2017年第3期68-73,共6页Tactical Missile Technology

摘　　要：研究了一类含动力再入飞行器的跳跃轨迹优化问题。首先将动力学方程中的控制量进行有限维数的参数化,减少优化算法的搜索空间。然后利用约束PSO算法和Powell优化算法相结合的混合优化算法求解满足再入过程约束和末端约束的最优滑翔轨迹。最后通过对高升阻比再入滑翔飞行器CAV-H的仿真分析,验证了该混合优化算法的有效性。The trajectory optimization problems in the glide segment of the powered reentry vehicle were discussed in this paper. First of all,a parameterized control variable profile with finite dimensions was developed in order to reduce the searching space of the optimization algorithm. Then,an improved algorithm of PSO and Powell was used to optimize the reentry gliding trajectory satisfying both the path and the terminal constraints. Finally,a numerical simulation was performed based on the high-lifting common aerial vehicle（ CAV-H） model,and the effectiveness of the method was demonstrated by the simulation results.

关 键 词：跳跃轨迹 粒子群优化 Powell优化 轨迹优化 

分 类 号：TJ765.3[兵器科学与技术—武器系统与运用工程]