摘要
针对运载火箭推力下降故障下难以实现在线弹道重构的问题,提出了一种基于神经网络的剩余运载能力估计及程序角重构的算法。以线下基于工程实践的弹道优化方法生成的故障状态下最优弹道作为学习样本,针对程序角曲线的特点,分别使用不同学习方式学习一级飞行段程序角和其他飞行段程序角;分析不同超参数对神经网络训练过程的影响,利用随机搜索法选取超参数。该算法使用以神经网络为核心的机器学习思想,用基于数据的方式避免了因运载火箭动力学模型复杂而无法在线快速求解最优弹道的问题,能够解决大气环境下弹道重构的难题。仿真结果表明,该算法对剩余运载能力估计准确,重构程序角与最优弹道相比误差小,运算速度相比其他方法优势明显。
A method for payload capacity estimation and program angle reconstruction is proposed based on neural networks to deal with the problem that it is difficult to reconstruct the trajectory after thrust drop.Optimal trajectories with thrust drop faults are generated offline based on engineering practice to train the neural networks.Features of the program angle are quite different in stage 1 and other stages.Different modes are taken.Impact of hyperparameters on the training process is also analyzed and a random search method is taken to optimize the hyperparameters.The proposed method is data-based and can avoid the complicated dynamics of the launch vehicle,and the trajectory reconstruction can even be used in endo-atmosphere.Numerical simulation shows that the proposed method can estimate the payload capacity accurately and the reconstructed program angle has a small error compared with the optimal trajectory.The arithmetic speed of the proposed method is also faster than those of other methods.
作者
张荣升
吴燕生
秦旭东
张普卓
ZHANG Rongsheng;WU Yansheng;QIN Xudong;ZHANG Puzhuo(Beijing Institute of Astronautical Systems Engineering,Beijing 100076,China;China Aerospace Science and Technology Corporation,Beijing 100048,China)
出处
《南京航空航天大学学报》
CAS
CSCD
北大核心
2021年第S01期25-31,共7页
Journal of Nanjing University of Aeronautics & Astronautics
关键词
推力下降
在线弹道重构
神经网络
超参数分析
thurst drop
online trajectory reconstruction
neural networks
analysis of hyperparameters
