Volume 40, Number 1, February 2022
|Page(s)||62 - 68|
|Published online||02 May 2022|
Research on parafoil optimal flight path planning algorithm for precise recovery of sub-stage booster of rocket
School of Automation, Northwestern Ploytechnical University, Xi'an 710072, China
Parafoil is an important novel method to realize the precise recovery of sub-stage booster of rocket. In this paper, a parafoil optimal path planning algorithm based on longicorn algorithm is proposed to solve the problems that the parafoil is easily affected by wind field and the error of landing point is large in the recovery of sub-stage booster of rocket. Firstly, according to the dynamics and kinematics equations of the parafoil and a rocket sub-stage combined system, a 6-degree-of-freedom model of the system was established to analyze the effects of different downward deviation on the forward velocity, vertical velocity and the trajectory planning of the parafoil during the homing process of the rocket sub-stage. On this basis, the number of circling turns, radius and azimuth of the cutting-high section of the combination of the parafoil and the sub-stage booster were taken as the optimal parameters, and the optimal path planning was carried out by using the longicorn algorithm. Finally, a homing path with comprehensive consideration of energy and the accuracy of the landing point was obtained. The simulation results show that the piecewise optimal flight path planning algorithm based on longicorn algorithm proposed in this paper has fast convergence speed and high precision of flight track landing point.
翼伞是实现运载火箭子级精确回收的一种新的重要手段, 针对翼伞在火箭子级回收中易受风场影响、落点误差大的问题, 提出了一种基于天牛群算法的翼伞最优航迹规划算法。根据翼伞和某型火箭一子级组合体的动力学和运动学方程建立六自由度模型, 并分析火箭一子级归航过程中不同转弯下偏量对前向速度、垂直速度及翼伞航迹规划的影响。在此基础上, 将翼伞一子级组合体盘旋削高段的盘旋圈数、盘旋半径和方位角作为寻优参数, 应用天牛群算法对组合体进行最优航迹规划, 将平均风的影响转换为目标点的飘移, 最终获得一条综合考虑能量以及落点精确度的归航航迹。仿真结果表明, 所提出的基于天牛群算法的火箭一子级翼伞回收分段最优航迹规划算法收敛速度快, 航迹落点精度高。
Key words: track planning / parafoil / first-stage booster of rocket / longicorn algorithm
关键字 : 航迹规划 / 翼伞 / 火箭一子级 / 天牛群算法
© 2022 Journal of Northwestern Polytechnical University. All rights reserved.
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