返回器飞行管道的快速预测算法OACSTPCD
Fast prediction algorithm of flight pipeline of reentry capsule
针对返回器回收任务中对安全空域和期望落点的计算需求,提出了基于Koopman算子的飞行管道快速预测算法,给出了搜救直升机安全飞行空域的判定流程.建立了物伞动力学模型,利用Halton采样方法从随机空间中均匀采点,计算得到多条可能弹道;采用Koopman算子的后拉机制,将初始概率密度值与当前状态关联,得到不确定条件下返回器及其分离部件的飞行管道和期望弹道.仿真结果表明,基于Koopman算子的飞行管道快速预测算法在收敛速度和精度上都要显著优于Monte Carlo方法;利用飞行管道计算结果对搜救直升机飞行路线进行规划后,碰撞风险最大降低54%且搜索时间减少70%.飞行管道预测算法已成功应用到嫦娥五号的回收任务中.
To meet the computational requirements of safe airspace and the expected landing point in the recovery mission of the reentry capsule,a fast prediction algorithm of the flight pipeline based on the Koopman operator approach was proposed for the reentry capsule and the determination process for safe flight airspace of search and rescue helicopters was provided.The body-parachute dynamic model was constructed.A group of discrete state points was uniformly selected from the random state space by using the Halton sampling method,and the multiple possible trajectory was calculated.Based on the back pulling mechanism of Koopman operator,the initial probability density value was associated with the current state to obtain the flight pipeline and desired trajectory of the reentry capsule and its separation parts under uncertain conditions.The simulation results show that the fast prediction algorithm of the flight pipeline based on the Koopman operator approach is significantly better than the Monte Carlo method in terms of convergence speed and accuracy.After using the flight pipeline calculation results to plan the flight route of the rescue helicopter,the collision risk is reduced by 54%at most and the corresponding search time is reduced by 70%.The proposed algorithm has been successfully applied to the Chang'e-5 recovery mission.
邹文;张国斌;丰志伟;涂国勇;禄晓飞;张青斌;杨涛
国防科技大学 空天科学学院,湖南 长沙 410073||湖南工商大学 计算机学院,湖南 长沙 410205国防科技大学 空天科学学院,湖南 长沙 410073中国人民解放军63620 部队,甘肃 酒泉 732750
降落伞回收技术不确定性分析Koopman算子返回器探月任务
parachute recovery technologyuncertainty analysisKoopman operatorreentry capsulelunar missions
《国防科技大学学报》 2024 (001)
22-31 / 10
国家自然科学基金资助项目(11902331)
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