基于PPO2强化学习算法的空间站轨道预报方法  

Orbit prediction method for space station based on PPO2 algorithm of reinforcement learning

作　　者：雷骐玮 张洪波[1] LEI Qiwei;ZHANG Hongbo(College of Aerospace Science and Engineering,National University of Defense Technology,Changsha 410073,China)

机构地区：[1]国防科技大学空天科学学院,长沙410073

出　　处：《中国空间科学技术》2023年第4期93-103,共11页Chinese Space Science and Technology

基　　金：装备预研航天科技联合基金(6141B060907)。

摘　　要：影响热层大气密度的因素较多且变化机理复杂,很难建立准确的大气模型,导致大气阻力摄动成为空间站轨道预报精度的主要影响因素之一。研究了基于PPO2强化学习算法的轨道预报方法,利用强化学习网络修正大气模型中的相关参数,提高了轨道预报精度。首先建立了空间站的轨道动力学模型,分析了大气模型参数的误差特性,设计了基于强化学习的轨道动力学模型修正方案。选择PPO2算法作为强化学习算法,设计了训练参量与强化学习网络模型,生成了PPO2算法的训练和测试样本,完成了仿真训练与测试。仿真结果表明,该方案能有效补偿大气密度模型不准确造成的轨道预报误差,提高空间站轨道预报的精度和效率。There are many factors affecting the atmospheric density of the thermosphere and the mechanism is complex.It is difficult to establish an accurate atmospheric model,resulting in the perturbation of atmospheric resistance,which has become one of the main factors affecting the orbit prediction accuracy of the space station.The orbit prediction method based on PPO2 algorithm of reinforcement learning was studied.The reinforcement learning network was used to modify the relevant parameters in the atmospheric model and improve the orbit prediction accuracy.Firstly,the orbital dynamics model of the space station was established,the error characteristics of the atmospheric model parameters were analyzed,and the orbital dynamics model modification scheme based on reinforcement learning was designed.PPO2 algorithm was selected as the reinforcement learning algorithm,the design of training parameters and reinforcement learning network model were completed,the training and test samples of PPO2 algorithm were generated,and the simulation training and test were completed.The simulation results show that the scheme could effectively compensate the orbit prediction error caused by the inaccuracy of atmospheric density model,and improve accuracy and efficiency of the orbit prediction of the space station.

关 键 词：大气阻力摄动 空间站 轨道预报 轨道动力学模型修正 PPO2算法 

分 类 号：V448.2[航空宇航科学与技术—飞行器设计]