火星探测出舱机构的识别定位与坡度测量

为实现火星探测两器分离过程中出舱机构的坡度测量,提出一种基于立体视觉技术的出舱机构识别定位与坡度测量方法。首先,采用特征匹配的方法对场景中的出舱机构进行识别定位。针对巡视器导航相机成像视角变化以及出舱机构倾角变化导致图像产生几何形变,火星表面光照因素的影响以及巡视器硬件的计算能力,并结合出舱机构自身特点,提出一种区域特征与Blob特征相融合的识别定位方法。采用加速鲁棒性特征（SURF）算法检测图像中的局部不变特征,并用最大稳定极值区域（MSER）算法检测图像最稳定极值区域,两种特征相融合之后构造SURF描述算子并进行特征匹配。同时,采用M估计抽样一致性（MSAC）算法计算对应点变换矩阵,实现出舱机构在场景中的初步定位。然后,通过对出舱机构上人工标志的精确定位以及三维重建,并采用主成分分析法进行平面拟合,实现出舱机构的坡度测量。试验结果表明,该方法对成像视角变化以及光照变化具有鲁棒性,提取的有效特征数以及计算时间均优于尺度不变特征变换（SIFT）算法,基本满足火星探测中出舱机构坡度测量要求。     

星载GPS数据质量控制方法研究

针对星载全球定位系统（GPS）数据的特点,分别从阈值设置和周跳检验量的角度对TurboEdit方法进行改进,并在传统抗差估计方法的基础上,提出一种改进的抗差估计策略。利用GRACE卫星的星载GPS观测数据对本文的质量控制方法进行分析验证,结果表明：改进的质量控制方法能明显增强周跳探测性能,有效抑制小周跳和粗差的影响,提高几何法定轨的精度和可靠性。     

FDF鲁棒决策方法研究

故障检测滤波器（ＦＤＦ）是故障检测与隔离（ＦＤＩ）方法中一种重要而系统的方法，但到目前为止有关这种方法的研究基本上都基于模型参数精确已知的情况，本文研究了具有模型误差时ＦＤＦ的鲁棒决策方法，得到了一些有意义的结论。     

Robust design and analysis of a conformal expansion nozzle with inverse-design idea

This paper examines robust optimization design and analysis of a conformal expansion nozzle of flying wing Unmanned Aerial Vehicle(UAV) with the inverse-design idea.In view of flow features and stealth constraints, the inverse-design idea is described and the uncertainty-based robust design model is presented.A robust design system employs this model to combine deterministic optimization and robust optimization and is applied into design of a conformal expansion nozzle.The results indicate that design optimization can conform to the anticipation of the inversedesign idea and significantly improve the aerodynamic performance that meet the requirement of 6σ.The present method is a feasible nozzle design strategy that integrates robust optimization and inverse-design.     

Variational method based robust adaptive control for a guided spinning rocket

In this paper, a robust adaptive controller is designed for a guided spinning rocket, whose dynamics presents the characteristics of pitch-yaw cross coupling, fast time-varying aerodynamics parameters and wide flight envelop. First, a coupled nonlinear six-degree-of-freedom equation of motion for a guided spinning rocket is developed, and the lateral acceleration motion is modeled as a control plant with time-varying matched uncertainties and unmodeled dynamics. Then, a robust adaptive control method is proposed by combining Bregman divergence and variational method to achieve fast adaption and maintain bounded tracking. The stability of the resulting closed-loop system is proved, and the ultimate bound and convergence rate are also analyzed. Finally, numerical simulations are performed for a single operating point and the whole flight trajectory to show the robustness and adaptability of the proposed method with respect to time-varying uncertainties and unmodeled dynamics.     

一种基于灰色预测理论和抗差自适应Kalman滤波的滑坡监测算法

针对当前的山体滑坡监测技术监测精度低、实时性差、自动化程度低的问题，提出了一种基于灰色预测理论和抗差自适应Kalman滤波的滑坡监测技术。该技术使用抗差自适应Kalman滤波技术,对包括实时动态(RTK）载波相位差分定位数据、无人机摄影测量数据、土工带传感器数据在内的多源数据进行融合分析，将滑坡形变监测精度提高到了mm级。RTK技术和土工带传感器的使用克服了天气状况、植被覆盖对滑坡监测的影响。使用灰色预测理论对山体滑坡监测点进行形变预测，结合蠕变切线角判据，该技术实现了对山体滑坡预警等级的划分。仿真实验结果显示，该山体滑坡监测技术能够成功实现山体滑坡预测预警功能。     

Snow depth retrieval by using robust estimation algorithm to perform multi-SNR and multi-system fusion in GNSS-IR

Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technology provides a new means of snow depth detection. Multi-satellite and multi-Signal-to-Noise Ratio (SNR) provide more data for daily high-precision snow depth retrieval, but also face the problem of data fusion and effective utilization. Therefore, this study proposes a robust estimation algorithm based on multi-satellite and multi-SNR fusion applied to the observations of a GNSS station in Alaska. This study uses four solutions (S_avg, S_med, S_{RE_avg} and S_{RE_med}) to carry out multi-system fusion snow depth inversion and precision comparison research. The S_avg has more obvious disadvantages, which is not suitable for snow depth assessment. The S_{RE_avg} and S_{RE_med} have better snow depth retrieval effects in the snowy periods. The correlation coefficient (R), root mean square error (RMSE) and mean error (ME) of the calculated snow depth using the robust estimation algorithm with respect to the nearest in-situ measurements reached 0.759, 3.7 cm and ?1.4 cm, respectively. Compared with the S_med, the R is increased by 2.0 %, the RMSE corresponds to an improvement of 2.6 %. Moreover, the ME of the snow depth retrievals, as an indicator of the measurement bias, has significantly decreased by 6.7 %. The result also shows that the snow depth inversion by the robust estimation algorithm is more consistent with the in-situ measurements, further extending and advancing the optimal algorithm for snow depth retrieval.     

A point cloud deep neural network metamodel method for aerodynamic prediction

Aiming to reduce the high expense of 3-Dimensional (3D) aerodynamics numerical simulations and overcome the limitations of the traditional parametric learning methods, a point cloud deep learning non-parametric metamodel method is proposed in this paper. The 3D geometric data, corresponding to the object boundaries, are chosen as point clouds and a deep learning neural network metamodel fed by the point clouds is further established based on the PointNet architecture. This network can learn an end-to-end mapping between spatial positions of the object surface and CFD numerical quantities. With the proposed aerodynamic metamodel approach, the point clouds are constructed by collecting the coordinates of grid vertices on the object surface in a CFD domain, which can maintain the boundary smoothness and allow the network to detect small changes between geometries. Moreover, the point clouds are easily accessible from 3D sensors. The point cloud deep learning neural network, which employs re-sampling technique, the spatial transformer network and the fully connected layer, is developed to predict the aerodynamic characteristics of 3D geometry. The effectiveness of the proposed metamodel method is further verified by aerodynamic prediction and robust shape optimization of the ONERA M6 wing. The results show that the proposed method can achieve more satisfactory agreement with the experimental measurements compared to the parametric-learning-based deep neural network.     

Robust optimal sliding mode control for the deployment of Coulomb spacecraft formation flying

Inter-spacecraft electrostatic force (Coulomb force) is desirable for close formation flying control because of its propellant-less and free contaminate characteristics attributed to the propellant exhaust emission. This paper presents robust optimal sliding mode control to deal with the problem of thruster saturation in tracking the formation trajectory for Coulomb spacecraft formation flying. The robust controller design is based on optimal control theory as a linear quadratic system, and it is augmented with an integral sliding mode control technique. The stability of the closed-loop system is guaranteed using the second Lyapunov method. The developed controller outperforms the existing ones, because it has a higher degree of fine-tuning to cope with the uncertainty. Numerical simulations are employed to confirm the efficiency of the developed controller.     

Stationkeeping of halo orbits in Jupiter-Europa-Io system

Europa is one of the most promising exploration targets in search for extraterrestrial life. In the observation of Europa, halo orbits are suitable locations, because they are periodic and three-dimensional, and stationary with respect to Europa. However, halo orbits are naturally unstable and thus need stationkeeping. This study addresses the stationkeeping problem of halo orbits in the Jupiter-Europa system perturbated by another Galilean moon Io, in which case Io’s mass and orbital rate are assumed to be unknown. A tight stationkeeping scheme is proposed while accounting for autonomous navigation. To deal with the unknown gravitational perturbation from Io, the mass and orbital rate of Io are estimated during the flight and are then used to enhance the control robustness and stability, and improve the navigation accuracy. The control saturation problem is addressed by introducing adjustable parameters into the control law. The accuracy and error distribution of estimation is evaluated through Monte Carlo simulation.