摘要
针对运用间接法进行弹道优化时存在共轭变量初值高度敏感难以估计而无法获得全局最优解的缺点,引入混合遗传算法对弹道优化时的共轭变量初值进行搜索,并求解获得具有最大横程的再入轨迹.求解时考虑了热流约束、过载约束和动压约束,约束的处理采用惩罚函数方法,通过对不可行解的惩罚转换为无约束问题.数值仿真验证了该算法实用性.
The indirect method cannot obtain the solution of global trajectory optimization, since the difficulty of evaluating initial values of conjugate variables which are highly sensitive. To overcome that disadvantage, a hybrid genetic algorithm is introduced in this paper to search initial value of conjugate variables and the equations are solved to obtain the reentry trajectory with maximum cross range. In the optimization process, the constraints of equations, such as heating rate, dynamic pressure and overload, could be handled as penalty functions which could convert the feasible solution to unconstrained problem by penalty conversion. Numerical simulations confirm the applicability of the presented algorithm and some typical spacecraft gliding trajectories are optimized.
出处
《北京理工大学学报》
EI
CAS
CSCD
北大核心
2013年第7期665-668,共4页
Transactions of Beijing Institute of Technology
关键词
再入飞行器
混合遗传算法
最大横程
再入轨迹
reentry vehicle
hybrid genetic algorithm
reentry trajectory
maximum cross range
