升力式飞行器助推段多约束弹道优化设计

Optimization Design of Trajectory in Boost Phase of Lift Vehicle With Multi-constraints

升力式飞行器助推段弹道设计面临着复杂大气飞行环境下多约束耦合条件下的运载能力优化难题,需要在满足分离高度、攻角量值与变化率限幅、入轨点高度与倾角等约束下,通过设计助推段程序角,使得入轨点速度最大。为了寻求一种快速解决这一问题的工程设计方法,以三级固体运载器为研究对象,提出了升力式飞行器助推段多约束弹道设计方法,通过设计助推段弹道模式制定了设计变量,梳理确定了助推段约束条件,建立了多约束下以入轨速度最大为目标的优化模型。通过分析设计变量与约束条件的耦合关系制定了高效的优化流程,并以牛顿迭代法确定优化初值,且以序列二次规划法开展优化仿真。仿真获得了满足多约束条件下的优化解,入轨速度提高了3.1%,验证了升力式飞行器助推段弹道设计方法的正确性和优化求解流程的有效性。升力式飞行器助推段多约束弹道优化设计方法具有较强的工程实用性,模型建立方法与优化求解流程可为其他优化问题提供参考。

The design of trajectory in boost phase of the lift vehicle is facing the difficult problem of carrying capacity optimization under the coupling condition of multiple constraints in the complex atmospheric flight environment.It is necessary to design the program angle of the boost phase to maximize speed of entering orbit under the constraints of stages separation height,angle of attack limit,height of the orbit entry point,etc.In order to find an engineering design method to quickly solve this problem,taking the three-stage solid launch vehicle as the research object,the multi-constraint trajectory design method in boost phase of the lift vehicle was put forward.The design variables were formulated by designing the flight trajectory mode of the boost phase,and the constraint conditions of the boost phase were determined.The optimization model aiming at the maximum orbital speed under multi constraints was built.By analyzing the coupling relationship between design variables and constraints,an efficient optimization process was formulated.The optimization initial value was determined by Newton iterative method,and the optimization simulation was carried out by sequential quadratic programming method.The optimal solution satisfies the multi-constraint conditions,and the orbit entry speed increases by 3.1%,which verifies the correctness of the trajectory design method and the effectiveness of the optimization process.The multi-constraint trajectory optimization design method in boost phase of lift vehicle has strong engineering practicability.The modeling method and optimization process can offer reference for other optimization problems.