基于深度增强学习的卫星姿态控制方法

针对卫星在执行丢弃载荷或捕获目标等复杂任务时遭遇的姿态突然发生变化的问题,采用深度增强学习方法对卫星姿态进行控制,使卫星恢复稳定状态。具体来说,首先搭建飞行器的姿态动力学环境,并将连续的控制力矩输出离散化,然后采用Deep Q Network算法进行卫星自主姿态控制训练,以姿态角速度趋于稳定作为奖励获得离散行为的最优智能输出。仿真试验表明,面向空间卫星姿态控制的深度增强学习算法能够在卫星受到突发随机扰动后稳定卫星姿态,并能有效解决传统PD控制器依赖被控对象质量参数的难题。所提出的方法采用自主学习的方式对卫星姿态进行控制,具有很强的智能性和一定的普适性,在未来卫星执行复杂空间任务中的智能控制方面有着很好的应用潜力。     

基于F-P腔的激光频率稳定传递方法

量子传感的发展需要频率高度稳定的激光器为基础,且实现大失谐激光频率稳定通常是提高其精度和灵敏度的关键。针对大失谐激光稳频问题,提出了一种利用法布里-珀罗（F-P）腔传递激光频率稳定性的方法。以饱和吸收稳频法锁定的激光器频率为参考,基于锁相原理,锁定F-P腔长度。利用F-P腔长度这个稳定的参考点,实现目标激光器的频率的精确锁定。实验将目标激光器波长锁定于767.001 nm,失谐频率为150 GHz,锁定后的频率漂移为1 MHz/h。该方法解决了激光大失谐稳频问题,对工程实践和科学研究有重要意义。      

Inverse identification of constitutive parameters of Ti2AlNb intermetallic alloys based on cooperative particle swarm optimization

Ti₂AlNb intermetallic alloy is a relatively newly developed high-temperature-resistant structural material, which is expected to replace nickel-based super alloys for thermally and mechanically stressed components in aeronautic and automotive engines due to its excellent mechanical properties and high strength retention at elevated temperature. The aim of this work is to present a fast and reliable methodology of inverse identification of constitutive model parameters directly from cutting experiments. FE-machining simulations implemented with a modified Johnson-Cook (TANH) constitutive model are performed to establish the robust link between observables and constitutive parameters. A series of orthogonal cutting experiments with varied cutting parameters is carried out to allow an exact comparison to the 2D FE-simulations. A cooperative particle swarm optimization algorithm is developed and implemented into the Matlab programs to identify the enormous constitutive parameters. Results show that the simulation observables (i.e., cutting forces, chip morphologies, cutting temperature) implemented with the identified optimal material constants have high consistency with those obtained from experiments, which illustrates that the FE-machining models using the identified parameters obtained from the proposed methodology could be predicted in a close agreement to the experiments. Considering the wide range of the applied unknown parameters number, the proposed inverse methodology of identifying constitutive equations shows excellent prospect, and it can be used for other newly developed metal materials.     

基于改进粒子群优化模糊控制的MPPT算法研究

在光照强度和温度变化时,常规的最大功率点跟踪(MPPT)算法难以快速准确地跟踪光伏系统最大功率点。针对此问题,设计了一种改进粒子群优化算法(PSO)的模糊控制器。首先,依据常规MPPT特性,设计了一种带调整因子的模糊控制算法以快速收敛到最大功率点；然后,采用参数自适应PSO对设计的模糊控制器调整因子进行动态优化。仿真结果表明:所设计的参数自适应PSO优化模糊控制器能快速准确地跟踪最大功率点,保证了MPPT的动态响应速度和稳态精度,提高了光伏系统的工作效率。     

A drilling tool design and in situ identification of planetary regolith mechanical parameters

The physical and mechanical properties as well as the heat flux of regolith are critical evidence in the study of planetary origin and evolution. Moreover, the mechanical properties of planetary regolith have great value for guiding future human planetary activities. For planetary subsurface exploration, an inchworm boring robot (IBR) has been proposed to penetrate the regolith, and the mechanical properties of the regolith are expected to be simultaneously investigated during the penetration process using the drilling tool on the IBR. This paper provides a preliminary study of an in situ method for measuring planetary regolith mechanical parameters using a drilling tool on a test bed. A conical-screw drilling tool was designed, and its drilling load characteristics were experimentally analyzed. Based on the drilling tool-regolith interaction model, two identification methods for determining the planetary regolith bearing and shearing parameters are proposed. The bearing and shearing parameters of lunar regolith simulant were successfully determined according to the pressure-sinkage tests and shear tests conducted on the test bed. The effects of the operating parameters on the identification results were also analyzed. The results indicate a feasible scheme for future planetary subsurface exploration.     

基于MapReduce的CME参数识别模型并行计算技术

日冕物质抛射（Coronal Mass Ejection，CME）参数识别模型是太阳风预报过程的重要组成部分.在空间环境预报业务中，为提高太阳风预报的准确率，需要提高CME参数识别的精度.模型以计算任务串行的方式运行，运算效率低导致模型运算时间长，不能满足这种需求.CME参数识别模型的物理运算过程相互不独立，其在单节点上的运行方式不能满足并行化要求.基于MapReduce的并行计算框架，改进了CME参数识别模型的计算流程，提出CDMR（CME detection under MapReduce）方法，实现了CME参数识别模型的并行计算，并对比分析CME参数识别模型在串行计算和MapReduce并行计算下的运行时间，提高了模型的识别精度和计算效率.      

基于新陈代谢GM（1,1）模型的导弹遥测故障预测算法

在人工监视遥测关键参数是否超出阈值范围以判断导弹故障的传统模式基础上,对GM（1,1）预测模型进行了改进,研究了基于新陈代谢的GM（1,1）预测算法对遥测参数实时预测效果.该算法能不断去掉最老的信息,充分利用最新的信息,避免了预测模型老化的现象;由于用于预测的数列维度较少,便于计算,能够保证算法的实时性.用MATLAB编程实现算法,比对实测数据的预测值与实际值,并进行误差分析.结果表明,该算法对遥测参数的预测精度较高,可为及时发现导弹故障提供科学的依据.     

孔挤压强化工艺参数对GH4169高温合金疲劳寿命影响分析

为探究孔挤压强化工艺参数对镍基高温合金GH4169低周疲劳寿命的影响规律,首先建立了经试验验证的孔挤压强化后GH4169带孔平板低周疲劳寿命模型,在此基础上研究了600℃、820MPa、应力比0.1条件下挤压量、前导角、后导角、摩擦因数、芯棒材料等典型工艺参数对孔挤压强化后疲劳寿命的影响规律。结果表明：提高挤压量能明显提升疲劳寿命,但过大的挤压量会导致疲劳寿命下降;增加前导角有助于改善挤入面疲劳寿命;后导角对疲劳寿命没有影响;摩擦因数的提高会对孔挤压强化效益产生负面影响;芯棒材料的屈服强度应大于被挤压材料。     

钛合金TC4的超硬磨料柔性抛光轮数控抛光试验研究

航空发动机叶片钛合金材料在数控磨抛加工中容易发生烧伤及黏附现象,针对常用的TC4材料开展了超硬磨料柔性抛光轮数控抛光试验研究。分析了超硬磨料柔性抛光轮抛光参数中转速、进给速度、预压量及行距对抛光去除深度及抛光后试件表面粗糙度的影响规律并通过正交试验分析了各抛光参数影响的主次关系。确认了钛合金试件抛光表面黏附物质成分,并同时分析了表面黏附物的形成原理。给出了TC4钛合金材料在超硬磨料柔性抛光轮数控抛光过程中工艺参数的选择策略,为TC4材料的航空发动机叶片和整体叶盘在超硬磨料柔性抛光轮数控抛光过程中提供理论依据和技术基础。     

双定子涡流制动器电磁参数对制动力矩的影响

介绍了双定子涡流制动器(DSECB)的工作原理。根据等效磁路法推导了DSECB制动力矩公式。利用有限元法对DSECB进行了3D仿真分析。详细分析了DSECB各电磁参数对制动力矩特性曲线的影响,为DSECB优化设计提供了参考依据。