Ground maneuver for front-wheel drive aircraft via deep reinforcement learning

The maneuvering time on the ground accounts for 10%–30% of their flight time, and it always exceeds 50% for short-haul aircraft when the ground traffic is congested. Aircraft also contribute significantly to emissions, fuel burn, and noise when taxiing on the ground at airports. There is an urgent need to reduce aircraft taxiing time on the ground. However, it is too expensive for airports and aircraft carriers to build and maintain more runways, and it is space-limited to tow the aircraft fast using tractors. Autonomous drive capability is currently the best solution for aircraft, which can save the maneuver time for aircraft. An idea is proposed that the wheels are driven by APU-powered (auxiliary power unit) motors, APU is working on its efficient point; consequently, the emissions, fuel burn, and noise will be reduced significantly. For Front-wheel drive aircraft, the front wheel must provide longitudinal force to tow the plane forward and lateral force to help the aircraft make a turn. Forward traction effects the aircraft’s maximum turning ability, which is difficult to be modeled to guide the controller design. Deep reinforcement learning provides a powerful tool to help us design controllers for black-box models; however, the models of related works are always simplified, fixed, or not easily modified, but that is what we care about most. Only with complex models can the trained controller be intelligent. High-fidelity models that can easily modified are necessary for aircraft ground maneuver controller design. This paper focuses on the maneuvering problem of front-wheel drive aircraft, a high-fidelity aircraft taxiing dynamic model is established, including the 6-DOF airframe, landing gears, and nonlinear tire force model. A deep reinforcement learning based controller was designed to improve the maneuver performance of front-wheel drive aircraft. It is proved that in some conditions, the DRL based controller outperformed conventional look-ahead controllers.     

基于光电脉冲的取样示波器上升时间校准实验研究

介绍了基于光电脉冲的取样示波器上升时间校准方法,设计组建了实验系统,对带宽为30GHz、50GHz取样示波器的上升时间分别进行了校准,并根据该方法建立了校准装置。对实验结果进行比较分析后,显示高斯型光电脉冲适用于具有高斯响应的取样示波器上升时间的校准。     

基于FPGA的硅微机械陀螺仪数字化驱动电路研究

为了进一步提高硅微机械陀螺仪的性能,提出了一种基于FPGA数字信号处理来实现控制的数字化驱动电路。该设计方案通过AGC环路稳定了驱动幅度,并用SPLL对相位进行了控制以跟踪谐振频率。两主要环路以FPGA为硬件基础,用软件编程实现,相较模拟驱动电路在实际应用中更具灵活性。实验给出了该电路的驱动幅度和频率漂移曲线,证明了该电路可以较好的实现硅微机械陀螺仪的驱动。     

输送管壁温对膏体推进剂燃速的影响

膏体推进剂作为一种新型特种推进剂,具有广阔的应用前景。膏体推进剂燃速直接影响发动机内弹道性能,研究输送管道壁温对推进剂燃速的影响具有重要意义。采用幂律本构方程表征膏体推进剂粘度,Arrhenius方程表征温度对粘度影响,并利用中心有限差分格式对控制方程进行离散。对恒定壁温下的膏体推进剂与管道间传热特性进行数值仿真,并进行了数值验证。结合仿真结果,并借鉴固体推进剂初温与燃速关系,分析了热管道内膏体推进剂燃速特性。结果表明：近壁面加热层厚度随入口速度增高而减小,随管径增大而增大,管内膏体推进剂平均燃速较入口温度条件下有所提高,同时,高温壁面条件下,近壁面推进剂温度高于爆发点,需要考虑管道内的防窜火措施。     

火星环境下降落伞拉直过程的动力学建模

文章在简要介绍火星探测器的物伞系统组成以及火星探测器降落伞拉直过程的基础上,根据拉直过程中各物体的运动属性,建立了火星探测器降落伞拉直过程的三维动力学模型。模型将伞包视为变质量六自由度刚体,进入器视为六自由度刚体,伞绳/伞衣采用质量阻尼弹簧模型,即将伞衣、伞绳、连接绳以及吊带离散成若干绳段,每个绳段处理为质量集中在端点的三自由度质点,各质点之间以阻尼弹簧相连。利用所提出的动力学模型对“海盗号”第一次气球发射试验进行了仿真,并与Moog R.D.的仿真计算结果和试验数据进行了对比,验证了该文数学模型的正确性与有效性。     

运载火箭气氧/煤油姿控发动机技术研究

为实现空间推进系统的无毒、无污染、低成本、高性能和高可靠性,在国内首次研制了运载火箭辅助动力系统气氧/煤油发动机。以推力150 N的气氧/煤油发动机为研究对象,给出了点火、喷注器及身部冷却、阀门等的设计方案。介绍了研制中突破的小姿控发动机电脉冲点火器、气/液组合的有效混合、发动机稳态工作时的烧蚀,以及高空真空点火等关键技术。计算了气液两相流稳态燃烧流场并进行了氧化剂路气流试验。地面热试车和高空模拟热试车的结果表明,电脉冲点火器可实现发动机的可靠点火,采用同轴离心式内混合喷注、铌合金液膜辐射冷却方案的该气氧/煤油发动机真空比冲可达2 800 N.s/kg,脉冲工作大于3 000次,但真空中发动机的冷却仍需进一步研究。     

气氧/煤油发动机电点火技术研究

根据姿控发动机小结构、多脉冲、快响应的工作特点,研制了国内创新的150 N姿控发动机电脉冲点火技术,为无毒、无污染、高性能、低成本的气氧/煤油非自燃推进剂发动机奠定了基础。给出了航天电脉冲点火器的参数、设计方案和工作原理。地面和模拟真空环境热试车结果表明,气氧/煤油发动机采用电脉冲点火技术方案可行,地面及高空环境中均能可靠点火。150 N气氧/煤油发动机真空比冲可达2 800 N.s/kg,脉冲工作次数大于3 000次,并有较强的脉冲工作潜力。电磁兼容性试验显示,电脉冲点火发动机与箭上控制系统具有较好的电磁兼容性。     

电磁脉冲武器对雷达的损伤效应及防护措施

介绍了电磁脉冲武器的特点,分析了电磁脉冲武器对雷达系统的损伤效应,提出了雷达的技术、战术防护措施。     

复连通区域曲线积分与路径无关的充要条件

本文主要给出了复连通区域曲线积分与路径无关的充要条件,同时进一步讨论了当曲线积分与路径相关时,沿不同路径的积分之间的某种关系。     

基于发射站触发脉冲延时的双站时间同步模型

提出了一种双基地雷达的时间同步方法模型。基于距离跟踪与处理,通过调整发射站发射机触发脉冲的延迟时间,可保证接收站在单站和双站工作时目标信号都能重合。仿真结果表明:该模型正确。