空间飞行器控制软件的动态自适应演化方法

空间飞行器在太空飞行过程中需要满足多任务、多工作模式以及大范围机动的需求, 其控制系统在大范围机动飞行条件下存在大量的外界干扰和内部参数不确定，同时飞行器的自适应过程受限于资源，人工干预难度大，并且现有的成熟的动态自适应方法并不一定适合空间飞行器控制软件进行自主控制，所以目前对自主控制系统软件的动态自适应调整方法提出了更高的要求.由此提出一种在双层感知—分析—决策—执行(MAPE)控制循环基础上的自适应框架，使用基于规则/策略的决策方法和基于强化学习的决策方法对系统感知到的局部和全局变化进行决策，并且采用基于数据驱动的反馈方法对规则库中的策略信息进行周期性的调整和优化，保证飞行器在太空执行任务面对复杂的环境时可以动态的完成自适应调整，保障任务的可靠执行.

Dynamic Adaptive Evolution Method for Control System of Space Vehicle

Spacecraft needs to meet the requirements of multi task, multi working mode and large scale maneuvering during space flight. Its control system has a lot of external interference and uncertain internal parameters under large scale maneuvering conditions. At the same time, the adaptive process of the aircraft is affected by limited resources, and manual intervention is difficult. The existing mature dynamic adaptive methods are not necessarily suitable for spacecraft autonomous control software, so the current dynamic adaptive adjustment methods of autonomous control system software cannot meet the higher requirements. Therefore, an adaptive framework based on the two layer perception analysis decision execution (MAPE) control loop is proposed, which uses rule/strategy based decision making methods and reinforcement learning based decision making methods to make decisions on local and global changes. In addition, a data driven feedback method is used to periodically adjust and optimize the policy information in the rule library to ensure that the aircraft can dynamically complete adaptive adjustments and ensure the reliable execution of tasks when performing tasks in complex space environments.