Locally generalised multi-agent reinforcement learning for demand and capacity balancing with customised neural networks

Reinforcement Learning (RL) techniques are being studied to solve the Demand and Capacity Balancing (DCB) problems to fully exploit their computational performance. A locally generalised Multi-Agent Reinforcement Learning (MARL) for real-world DCB problems is proposed. The proposed method can deploy trained agents directly to unseen scenarios in a specific Air Traffic Flow Management (ATFM) region to quickly obtain a satisfactory solution. In this method, agents of all flights in a scenario form a multi-agent decision-making system based on partial observation. The trained agent with the customised neural network can be deployed directly on the corresponding flight, allowing it to solve the DCB problem jointly. A cooperation coefficient is introduced in the reward function, which is used to adjust the agent’s cooperation preference in a multi-agent system, thereby controlling the distribution of flight delay time allocation. A multi-iteration mechanism is designed for the DCB decision-making framework to deal with problems arising from non-stationarity in MARL and to ensure that all hotspots are eliminated. Experiments based on large-scale high-complexity real-world scenarios are conducted to verify the effectiveness and efficiency of the method. From a statistical point of view, it is proven that the proposed method is generalised within the scope of the flights and sectors of interest, and its optimisation performance outperforms the standard computer-assisted slot allocation and state-of-the-art RL-based DCB methods. The sensitivity analysis preliminarily reveals the effect of the cooperation coefficient on delay time allocation.     

载人航天器太阳翼多姿态输出能力建模及分析

载人航天器太阳翼在轨运行期间除正常对日定向转动外，根据飞行任务要求可能需要工作在α轴水平归零或垂直归零等特殊状态。在特殊工作模式下，太阳入射角和太阳翼的输出能力变化曲线更加复杂。建立太阳入射角与轨道角、太阳高度角、β轴转角等变量的数学模型，分析太阳入射角的变化规律，并结合光照区时长，建立太阳翼在轨输出能力的预测模型。与实际飞行数据相比较，表明该模型可较为准确地评估特殊工作模式下太阳翼的输出能力，可用于在轨整舱能量平衡分析，为飞行任务计划安排提供参考。     

运载火箭遥测数据智能判读算法应用

在运载火箭高发射密度、高判读需求、高数据量的背景下，现有自动化判读的判据覆盖率不全、判据编写门槛高、耗时多的问题日益凸显，缺少较通用的算法对传统判读算法未覆盖的判读任务进行判读补充，进而影响运载火箭效果评估与系统性能评定。为充分挖掘海量遥测数据中隐含的参数变化规律，设计智能判读算法作为传统算法的有益补充，提升传统判读的判读覆盖率和判读效率。以液体运载火箭长期加电试验产生的遥测数据为研究对象，设计集成神经网络智能判读算法，在给出的判读指标下研究得出，集成神经网络在频率异常、丢帧等五种现有判据难以描述的判读场景下，判读性能提升30%，提高了现有判据的覆盖率，后续可为判读体系完善和智能判读落地提供研究参考。     

试验运载器姿控总体优化设计技术

试验运载器在姿控动力系统空间布置紧张、控制力臂有限的情况下，要实现初始起控阶段大初始姿态偏差及气动干扰条件下的稳定控制，以及有效载荷释放阶段的多通道多次连续大角度调姿控制，给姿控系统的设计造成了困难。为此，通过鸭式直接力控制方案设计、多通道连续大角度调姿方案设计等技术，设计出了满足任务要求且性能良好的姿控总体方案。     

Autonomous mission reconstruction during the ascending flight of launch vehicles under typical propulsion system failures

In recent years, Chinese Long March(LM) launchers have experienced several launch failures, most of which occurred in their propulsion systems, and this paper studies Autonomous Mission Reconstruction(AMRC) technology to alleviate losses due to these failures. The status of the techniques related to AMRC, including trajectory and mission planning, guidance methods,and fault tolerant technologies, are reviewed, and their features are compared, which reflect the challenges faced by AMRC technology...     

卫星蜂窝夹层结构新型埋件工艺

针对卫星蜂窝夹层结构，研究了新型后埋件及其自动化后埋工艺，并与我国卫星领域传统后埋技术对比，考察了两种埋件技术对埋件承载力与承载系数的影响。结果表明，两种埋件系统承载模式基本一致，胶黏剂的分布状态对其承载性能有重要影响。新型后埋系统承载能力已经达到传统后埋系统的158.2%。新型后埋件技术更适合自动化方式，保证工艺稳定性一致性可靠性。