面向巨型星座系统的多地面站协同测控技术

Multi-Ground Station Collaborative Measurement and Control Technology for Giant Constellation System

测控技术是保障星座系统高效运维和管理的关键技术。近年来,随着星座规模的不断扩大,逐步形成了巨型星座系统,使得对星座的测控需求呈现爆发式的增长,从而对星座系统测控任务的完成量提出了新的要求。首先分析巨型星座系统测控任务约束和地面测控站设备约束并给出问题建模;其次提出一种地面站Agent基于学习规划片段的交互方式,通过引入约束惩罚算子和多地面站联合惩罚算子设计优化的目标函数;最后,提出一种多地面站Agent强化学习算法以求解多地面站协同任务分配策略。仿真结果显示,任务规模较大时,在文中提到的不同场景下该方法较传统算法有12%~20%的增益。

Measurement and control technology is the key technology to ensure the effi cient operation,maintenance and management of the constellation system.In recent years,with the continuous expansion of the constellation scale,mega-constellation system has gradually formed,which makes the demand for constellation measurement and control show an explosive growth,which puts forward new requirements for the completion of the constellation system measurement and control tasks.Firstly,the constraints of the mega-constellation system measurement and control tasks and the equipment constraints of the ground measurement and control station were analyzed,and the problem modeling was given;Secondly,an interaction method of the ground station agent based on the learning planning segment was proposed,by introduced the constraint penalty operator and the multi-ground station joint the penalty operator was designed to optimized the objective function.Finally,a multi-ground station Agent reinforcement learning algorithm was proposed to solved the multi-ground station cooperative task assignment strategy.Simulation experiments showed that when the task scale was large,the method had a gain of 12%~20%compared with the traditional algorithm in the diff erent scenarios mentioned.