基于乐观优化的平流层浮空器驻留控制方法  

Station-keeping Control for Stratospheric Aerostat though Optimistic Optimization

作　　者：范袁侨 邓小龙 杨希祥[1] 龙远 柏方超 FAN Yuanqiao;DENG Xiaolong;YANG Xixiang;LONG Yuan;BAI Fangchao(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：《无人系统技术》2024年第5期65-77,共13页Unmanned Systems Technology

基　　金：国家自然科学基金(52272445,52372438);湖南省自然科学基金(2023JJ30636);湖南省杰青资助项目(2023JJ10056)。

摘　　要：平流层浮空器可在20 km左右高空持久驻留,具有效费比高、驻空时间长、部署时间快等优点,是空天地一体化信息网络的重要组成部分。针对平流层浮空器在动态风场中的驻留控制问题开展研究。首先使用基于UNet的神经网络对平流层风场进行预测;其次提出了一种基于同时乐观优化的滚动时域优化控制器,该控制器适用于离散动作空间的规划问题,并针对禁飞区域和安全等约束进行改进;然后设计了一个奖励函数来权衡功耗和覆盖性能;最后完成了对2个位置和10个时间的仿真试验分析。结果表明,该算法可充分利用风场和气球动力学模型的信息,控制能力分别在90%和40%的情况下显著优于传统的贪心算法和A*算法,且能够较好地平衡能源管理和驻留性能,所提方法为其它平台的离散动作空间的最优控制问题求解提供参考。Stratospheric aerostats serve as cost-effective platforms for long-duration wireless communication at an altitude of 20 km.This study presents a receding horizon controller based on wind and balloon models to optimize the station-keeping performance.First,a neural network based on UNet is utilized for forecasting short-term wind fields.Then,an online receding horizon controller based on simultaneous optimistic optimization(SOO)is proposed for trajectory planning,which is particularly suitable for discrete action space.The SOO is modified and a reward function is designed to comply with restricted flight zone constraints and balance power consumption and station-keeping performance.Simulations across various positions and times demonstrate that the proposed method outperforms 90%and 40%cases separately compared to traditional greedy and A*algorithms,while make full use of the information of the wind.Besides,the reward function is effective to balance the station-keeping performance and the power consumption.The algorithm is potential for other optimal control problems with discrete action space.

关 键 词：平流层浮空器 驻留控制 乐观优化 滚动时域优化 导航 风速预测 深度学习 

分 类 号：V273[航空宇航科学与技术—飞行器设计]