1种节能环保带喷射泵的排气引射器设计方案

提出了1种新式带喷射泵的排气引射器设计方案。通过计算表明，在高空模拟试验台中应用带有喷射泵的引射器，可以将湿燃气中的水蒸气冷凝分离出来，减少次流流量，降低排气温度，大幅度提高燃气总压，从而提高引射系数，并可实现无污染排放。     

随机扰动下的航天器姿轨保持自抗扰控制

空间飞行器在轨运行过程中除受空间摄动外，还因飞行器任务需要产生随机扰动力和扰动力矩。针对空间飞行器受随机扰动产生的耦合运动控制问题，提出了利用自抗扰方法进行轨道保持和姿态稳定的控制方法。通过引入二阶线性扩展状态观测器，对系统总扰动和状态进行观测。结合PD控制方法结合总扰动前馈补偿，克服空间主要摄动及飞行器本身产生的随机扰动，实现轨道保持和姿态稳定。仿真试验结果表明:该方法可以有效克服总扰动的影响，实现姿轨协同控制。     

A novel visual servo method for space manipulators by planning jerk

A novel trajectory planning method for space manipulators is proposed in this article, which can generate trajectory in Cartesian space with continuous joint jerk. The key idea is that, given the desired position for an individual joint, the corresponding joint trajectory is generated in a way like a controller. The generated jerk acts as the controller’s output driving an ideal third-order system to arrive at the desired position, with no need for discrete points in advance. In real applications, the visual servo task is accomplished hierarchically. Since the desired pose in Cartesian space measured by cameras concerns multi degrees of freedom (DOF), desired positions for individual joints are obtained by inverse-kinematics model. Then, joint trajectories are generated as above. To improve the trajectory’s smoothness, a bridging matrix is implemented to ensure that the desired pose varies continuously. Simulation and experimental results show that the proposed method is effective to track targets with different kinds of motion, i.e. can track the input-bounded signal asymptotically.     

Advanced analytical model for orbital aerodynamic prediction in LEO

The growing interest in low earth orbit (LEO) applications demands for accurate modeling of orbital aerodynamics. But classical analytical models of aerodynamic coefficients in free molecule flow, such as the Sentman’s model, Schamberg’s model and Schaaf-Chambre model, were built upon over simplistic gas-surface interaction models, which degrade the fidelity of aerodynamic prediction. This work presents a new analytical model of orbital aerodynamic coefficients based on the state-of-the-art Cercignani–Lampis–Lord (CLL) gas-surface interaction model, where lobular quasi-specular scattering pattern and separate accommodation degree for different velocity components can be well captured. A key component of the new model is a rigorous function approximation solution of the reflected normal momentum flux based on the CLL model which is derived for the first time and is validated within 1% for any hypothermal flow and surface accommodation conditions. Closed-form analytical solutions of aerodynamic coefficients for simple convex geometries are obtained and exhibit high accuracy (within 0.1%) in typical LEO scenarios. The new analytical model surpasses the classical models in some important aspects, such as overcoming the diffuse scattering hypothesis constraint, considering the variation of normal momentum exchange with the surface incidence angle and being applicable in any hypothermal flow situation. In virtue of the advanced CLL model and feasibility of coupling with the panel method technique, the new analytical model is promising to provide more accurate predictions on the orbital aerodynamic coefficients for LEO applications.     

Orbital error propagation considering atmospheric density uncertainty

Due to the influence of various errors, the orbital uncertainty propagation of artificial celestial objects while orbit prediction is required, especially in some applications such as conjunction analysis. In the orbital error propagation of artificial celestial objects in low Earth orbits (LEOs), atmospheric density uncertainty is one of the important factors that require special attention. In this paper, on the basis of considering the uncertainties of position and velocity, the atmospheric density uncertainty is also taken into account to further investigate the orbital error propagation of artificial celestial objects in LEOs. Artificial intelligence algorithms are introduced, the MC Dropout neural network and the heteroscedastic loss function are used to realize the correction of the empirical atmospheric density model, as well as to provide the quantification of model uncertainty and input uncertainty for the corrected atmospheric densities. It is shown that the neural network we built achieves good results in atmospheric density correction, and the uncertainty quantization obtained from the neural network is also reasonable. Moreover, using the Gaussian mixture model - unscented transform (GMM-UT) method, the atmospheric density uncertainty is taken into account in the orbital uncertainty propagation, by adding a sampled random term to the corrected atmospheric density when calculating atmospheric density. The feasibility of the GMM-UT method considering atmospheric density uncertainty is proved by the further comparison of abundant sampling points and GMM-UT results (with and without considering atmospheric density uncertainty).