基于粒子群优化的再入飞行器在线轨迹规划

Onboard Trajectory Planning for Entry Vehicle Based on Particle Swarm Optimization

为应对飞行器再入过程中异常事件的发生,提出一种采用粒子群优化(PSO)的在线轨迹规划方法。推导了吸热量的解析表达式,基于航程、吸热量的解析预测以及参考剖面与再入走廊边界最小距离的解析计算,将总吸热量约束、终端位置约束,以及热流率、法向气动过载、动压和平衡滑翔条件等过程约束转为对参考剖面节点坐标的限制。根据参考剖面连续性与光滑性原则,减少选用的节点坐标数,设计关于剩余坐标的性能指标函数。为提高PSO效率,对速度更新算法进行改进。仿真结果表明改进后的优化算法实时性好,在线规划的轨迹能较好地满足飞行任务要求。

A novel onboard trajectory planning approach based on particle swarm optimization was developed for reentry vehicle to deal with anomalous events during reentry in this paper. The analytical expression for accumulated heat load was obtained. With the analytical prediction of range, accumulated heat load and analytical computation of the minimum distance from the reference profile to the boundaries of entry corridor, the constraints on terminal position, accumulated heat load, heating rate, normal aerodynamic load, dynamic pressure and equilibrium glide condition would be translated into the coordinate limits of the reference profile nodes. The number of the coordinates to be optimized should be reduced to keep the reference profile continuous and smooth. The performance index was designed as a function of these left coordinates. To improve the efficiency of particle swarm optimization, the velocity update algorithm was modified. The simulation results showed that the modified optimization algorithm was good real-time and the trajectories planned onboard could meet the requirements of flight task.