摘要
飞行器再入轨迹优化是一类最优控制问题。传统的优化方法存在初始值敏感问题,利用改进的基于小生境技术和精英方法的适应值共享拥挤遗传算法进行RLV的再入轨迹优化设计。以终端时间固定的空间最小控制能量再入轨迹和终端时间自由的平面最小热载再入轨迹为例,详细讨论了遗传算法用于再入轨迹优化设计所需要解决的一些关键问题。仿真结果表明提出的方法能够较快地搜索到全局最优解,对初始猜测值不敏感,能够方便用于RLV的再入轨迹方案选择和优化设计。
Spacecraft reentry trajectory optimization is a kind of optimal control problem. The conventional direct and indirect methods are very sensitive to the initial guess, which need much experience. The improved float point algorithm, Fitness Sharing Genetic Algorithms （FSGA） based on niche and elitism methods were used to solve this problem. Taking the minimum control energy reentry trajectory with fixod terminal time and the minimum total heat load reentry trajectory as examples, some key problems such as parameterized method were discussed in detail. Simulation results indicate that genetic algorithm can solve this problem and find the global optimal value efficiently , and it＇s insensitive to the initial guess. The optimal reentry trajectory can meet the requirements of attitude, heat flux and velocity.
出处
《固体火箭技术》
EI
CAS
CSCD
北大核心
2006年第4期235-238,265,共5页
Journal of Solid Rocket Technology
基金
sponsoredbyinnovationfoundationofNorthwesternPolytechnicalUniversity(CX200403)
