登月舱软着陆的非线性神经元控制

本文针对登月舱软着陆过程的控制问题，提出了一种非线性动态逆与状态反馈控制相结合的神经元控制系统设计方案。该方案包含两分：（１）借助前馈神经元网络通过学习逼近任意非线性映射的能力，建立被控系统的非线性动态逆神经元模型，用神经元网络实现被控非线性系统的线性化；（２）在线性化模型的基础上构造系统的神经元最优状态反馈控制器。本文给出的仿真结果显示出神经计算学在航天飞行器控制问题中所具有的潜在能力。     

降落伞缩距软着陆技术的研究及其进展

降落伞缩距软着陆技术是过去十多年中，美国在研的一种先进的缓冲减速技术。该技术通过在货物即将触地之前强力收缩货物吊带，产生较高拉力，使货物（回收物，空投物）和降落伞间距缩短，随之使货物减速，实现软着陆。经过近十多年研究，出现了几种典型的缩距器设计，包括活塞一滑轮缩距器，马达一绞盘缩距器，以及气动肌缩距器。目前的研究集中在空投应用方面，空投质量从验证概念时的几十千克，逐步发展到9100kg，并实现了快速装卸。作为气动减速的一种先进技术，降落伞缩距软着陆技术对于航天器回收与着陆系统的研究有着借鉴和参考价值，并具有潜在的应用可能。文章对降落伞缩距软着陆技术的研完发展概况以及几种典型设计进行了介绍。     

深空着陆器随机优化制导律设计

针对导航估计误差对制导精度的不利影响,提出了一种随机优化制导设计方法。该方法将导航估计性能作为优化项引入制导设计过程中,通过对观测轨迹的优化设计,使视觉导航估计精度得到提高,进而改善了制导性能;同时,引入单步超前优化方法减少迭代运算量。将其应用于深空探测着陆任务,仿真结果表明与传统估计最优制导方法相比,该随机优化制导律有效降低了估计误差给制导系统带来的不利影响,使着陆器导航与制导系统整体性能得到改善提高。     

ADAMS用户子程序在软着陆动力学仿真中的应用

软着陆是实施月球表面探测的一项关键技术,为得到月球着陆器的着陆性能,采用ADAMS软件进行动力学仿真。由于着陆器缓冲装置中常采用铝蜂窝结构,且它具有独特的力学特性,仿真时采用了用户子程序,通过在“阿波罗”试验模型中加载子程序实现了系统仿真。最后将仿真结果和相关数据比较,验证了该子程序和建模方法的正确性。     

基于图像的着陆点评估及探测器运动估计方法

针对深空探测任务特点给出了一种基于导航图像的探测器着陆点选择与探测器运 动估计技术。考虑到在着陆初始阶段(探测器高度＞2 km)导航相机所拍摄到的天体表面 近似为平面。这里通过跟踪至少4个平面内的特征点在导航图像序列 中的位置, 利用最小二乘法计算单应矩阵,由该矩阵得到探测器部分相对运动参数。结合激光测距仪给 出的探测器与天体表面之间的距离信息,可以得到全部6自由度参数。安全着陆点的选择与 评估首先利用图像灰度预选出平整的着陆点,再由着陆点对应的单应矩阵和之前获得的帧间 相对运动验证着陆点坡度。蒙特卡洛仿真结果表明,着陆点评估算法能够有效地选取满足平 整和坡度要求的着陆点,运动估计算法实时地对探测器参数进行估计,平移估计相对误 差小于10％,旋转估计误差小于0.23°。
     

月球探测器软着陆机构发展综述

综述了月球探测器软着陆机构的发展概况;分析了国际上月球探测器软着陆机构的结构组成、工作原理及特点。对不同展开机构和缓冲器特点进行了分析,预测了月球探测器软着陆机构的发展趋势。     

变推力月球软着陆制导优化研究

提出了一种新的使用变推力火箭发动机实现月球定点软着陆制导的优化方法.在软着陆加速度抛物线(即二次函数)变化的条件下,通过一组代数方程连接初始条件和终端条件,避免了求解两点边值问题的迭代计算.给出了瞬时位置速度状态参数以及需要推力加速度、推力和秒流量的计算公式,并通过调整总飞行时间和着陆点位置实现了燃料消耗最小的优化处理.算例结果证明了这种方法的可行性和有效性.     

一种基于软卡方检测的自适应Kalman滤波方法

在传统Kalman滤波中,卡方检测方法简单地将量测划分为正常和异常两种,针对其不足之处,改进并提出了一种新的软卡方检测方法。新方法根据卡方检测结果构造连续变化的量测,利用权重系数,充分挖掘处于正常值与异常值之间的可疑量测新息,同时还将该方法推广成多维量测的多分量卡方检测形式,建立了全面完整的软卡方检测Kalman滤波量测更新方程。最后,通过惯导/卫导组合导航仿真,验证了软卡方检测Kalman滤波的优势:无需任何参数调整且具有比Sage-Husa自适应滤波更小的统计误差波动。     

A multi-parameter model of heat treatment process for soft magnetic materials on performance of HSERs

Material properties play an important role in the performance of electromagnetic mechanism. For an aeronautic Hermetically-Sealed Electromagnetic Relay (HSER), more than 50% parts are made of soft magnetic materials. Therefore, the performance of soft magnetic materials directly determines the static and dynamic characteristics of the HSER. Based on the theory of crystal recrystallization, this paper analyzes cold extrusion and heat treatment in the processing of soft magnetic materials, simulates the grain change process of an armature at different heat treatment temperatures, establishes a correlation model of temperature, grain size, and magnetic energy, and verifies results by scanning electron microscopy. Results of heat treatment temperatures from 800 °C to 920 °C are obtained and compared. A sample soft magnetic material after heat treatment at different temperatures has the largest difference in the initial magnetization range, up to 22%. In order to verify the fluctuation of the overall output characteristics of an HSER caused by the difference between soft magnetic materials, a static and dynamic analysis model of a typical HSER is established, and the accuracy of the model is verified by a set of actual test system. The difference of dynamic characteristics under different heat treatment temperatures is nearly 3%.     

Measurement-driven Gauss-Hermite particle filter with soft spatiotemporal constraints for multi-optical theodolites target tracking

Multi-Optical Theodolite Tracking systems (MOTTs) can stealthily extract the target’s status information from bearings only through non-contact measurement. The constrained MOTTs are partially compatible, yet many existing research works and results are based on the known model, ignoring its discrimination with the target maneuvering behavior pattern. To compensate for these mismatches, this paper develops a Measurement-driven Gauss-Hermite Particle Filter (MGHPF), which elegantly fuses the spatiotemporal constraints and its soft form to perform MOTT missions. Specifically, the target dynamic model and tracking algorithm are based on the target behavior pattern with the adaptive turn rate, fully exploiting the spatial epipolar geometry characteristics for each intersection measurement by a minimax strategy. Then, the center of the feasible area is approximated via the analytic coordinate transformation, and the latent samples are updated via the deterministic Gauss-Hermite integral method with the target’s predictive turn rate. Simultaneously, the effects of truncation correction and compensation feedback from the current measurement and historical estimation data are adaptively incorporated into the PF’s importance distribution to cover the mixture likelihood. Besides, an effective causality-invariant updating rule is provided to estimate the parameters of these soft spatiotemporal constrained MOTTs with convergence guarantees. Simulated and measured results show good agreement; compared with the state-of-the-art Multi-Model Rao-Blackwell Particle Filter (MMRBPF), the proposed MGHPF improves the filtering accuracy by 7.4%-34.7% and significantly reduces the computational load.