大型工程装备可拓制造模型研究

在大型工程装备快速变批量生产需求环境下,基于可拓协同制造资源概念,提出了装备可拓制造"超网络"链制造模式,研究了可拓制造能力单元的RKCQ流模型,构建了可拓制造"超网络"链的参考模型与组建过程模型,并指出使能技术框架体系,可拓制造"超网络"链模式提出的目的在于为大型工程装备制造系统应对不确定性的需求提供制造能力储备.     

Differential code bias estimation based on uncombined PPP with LEO onboard GPS observations

Differential Code Bias (DCB) is an essential correction that must be provided to the Global Navigation Satellite System (GNSS) users for precise position determination. With the continuous deployment of Low Earth Orbit (LEO) satellites, DCB estimation using observations from GNSS receivers onboard the LEO satellites is drawing increasing interests in order to meet the growing demands on high-quality DCB products from LEO-based applications, such as LEO-based GNSS signal augmentation and space weather research. Previous studies on LEO-based DCB estimation are usually using the geometry-free combination of GNSS observations, and it may suffer from significant leveling errors due to non-zero mean of multipath errors and short-term variations of receiver code and phase biases. In this study, we utilize the uncombined Precise Point Positioning (PPP) model for LEO DCB estimation. The models for uncombined PPP-based LEO DCB estimation are presented and GPS observations acquired from receivers onboard three identical Swarm satellites from February 1 to 28, 2019 are used for the validation. The results show that the average Root Mean Square errors (RMS) of the GPS satellite DCBs estimated with onboard data from each of the three Swarm satellites using the uncombined PPP model are less than 0.18 ns when compared to the GPS satellite DCBs obtained from IGS final daily Global Ionospheric Map (GIM) products. Meanwhile, the corresponding average RMS of GPS satellite DCBs estimated with the conventional geometry-free model are 0.290, 0.210, 0.281 ns, respectively, which are significantly larger than those obtained with the uncombined PPP model. It is also noted that the estimated GPS satellite DCBs by Swarm A and C satellites are highly correlated, likely attributed to their similar orbit type and space environment. On the other hand, the Swarm receiver DCBs estimated with uncombined PPP model, with Standard Deviation (STD) of 0.065, 0.037 and 0.071 ns, are more stable than those obtained from the official Swarm Level 2 products with corresponding STD values of 0.115, 0.101, and 0.109 ns, respectively. The above indicates that high-quality DCB products can be estimated based on uncombined PPP with LEO onboard observations.     

Design and operation of an anaerobic digester for waste management and fuel generation during long term lunar mission

Waste treatment and management for manned long term exploratory missions to moon will be a challenge due to longer mission duration. The present study investigated appropriate digester technologies that could be used on the base. The effect of stirring, operation temperature, organic loading rate and reactor design on the methane production rate and methane yield was studied. For the same duration of digestion, the unmixed digester produced 20–50% more methane than mixed system. Two-stage design which separated the soluble components from the solids and treated them separately had more rapid kinetics than one stage system, producing the target methane potential in one-half the retention time than the one stage system. The two stage system degraded 6% more solids than the single stage system. The two stage design formed the basis of a prototype digester sized for a four-person crew during one year exploratory lunar mission.     

Adaptive neural network control of nonlinear systems with unknown dynamics

In this study, an adaptive neural network control approach is proposed to achieve accurate and robust control of nonlinear systems with unknown dynamics, wherein the neural network is innovatively used to learn the inverse problem of system dynamics with guaranteed convergence. This study focuses on the following three contributions. First, the considered system is transformed into a multi-integrator system using an input–output linearization technique, and an extended state observation technique is used to identify the transformed states. Second, an iterative control learning algorithm is proposed to achieve the neural network training, and stability analysis is given to prove that the network’s predictions converge to ideal control inputs with guaranteed convergence. Third, an adaptive neural network controller is developed by combining the trained network and a proportional-integral controller, and the long-standing challenge of model-based methods for control determination of unknown dynamics is resolved. Simulation results of a virtual control mission and an aerospace altitude tracking mission are provided to substantiate the effectiveness of the proposed techniques and illustrate the adaptability and robustness of the proposed controller.     

Automated design architectures for co-orbiting spacecraft swarms for planetary moon mapping

This work describes the design and optimization of spacecraft swarm missions to meet spatial and temporal visual mapping requirements of missions to planetary moons, using resonant co-orbits. The algorithms described here are a part of Integrated Design Engineering and Automation of Swarms (IDEAS), a spacecraft swarm mission design software that automates the design trajectories, swarm, and spacecraft behaviors in the mission. In the current work, we focus on the swarm design and optimization features of IDEAS, while showing the interaction between the different design modules. In the design segment, we consider the coverage requirements of two general planetary moon mapping missions: global surface mapping and region of interest observation. The configuration of the swarm co-orbits for the two missions is described, where the participating spacecraft have resonant encounters with the moon on their orbital apoapsis. We relate the swarm design to trajectory design through the orbit insertion maneuver performed on the interplanetary trajectory using aero-braking. We then present algorithms to model visual coverage, and collision avoidance in the swarm. To demonstrate the interaction between different design modules, we relate the trajectory and swarm to spacecraft design through fuel mass, and mission cost estimations using preliminary models. In the optimization segment, we formulate the trajectory and swarm design optimizations for the two missions as Mixed Integer Nonlinear Programming (MINLP) problems. In the current work, we use Genetic Algorithm as the primary optimization solver. However, we also use the Particle Swarm Optimizer to compare the optimizer performance. Finally, the algorithms described here are demonstrated through numerical case studies, where the two visual mapping missions are designed to explore the Martian moon Deimos.     

Nonlinear filtering for sequential spacecraft attitude estimation with real data: Cubature Kalman Filter,Unscented Kalman Filter and Extended Kalman Filter

This article compares the attitude estimated by nonlinear estimator Cubature Kalman Filter with results obtained by the Extended Kalman Filter and Unscented Kalman Filter. Currently these estimators are the subject of great interest in attitude estimation problems, however, mostly the Extended Kalman Filter has been applied to real problems of this nature. In order to evaluate the behavior of the Extended Kalman Filter, Unscented Kalman Filter and Cubature Kalman Filter algorithms when submitted to realistic situations, this paper uses real data of sensors on-board the CBERS-2 remote sensing satellite (China Brazil Earth Resources Satellite). It is observed that, for the case studied in this article, the filters are very competitive and present advantages and disadvantages that should be dealt with according to the requirements of the problem.     

Optimal mission planning of the reconfiguration process of satellite constellations through orbital maneuvers: A novel technical framework

In this paper, a general new methodology is presented for the orbital reconfiguration of satellite constellations on the basis of Lambert targeting theorem. In view of the cost and risk reduction, it is very important to consider the problem of satellite constellation reconfiguration with the two constraints of overall mission cost minimization and the desired final configuration. Hence, the dependent non-simultaneous deployment approach is proposed to minimize overall fuel cost. Despite the fact that the satellites deploy in a non-simultaneous manner, supplementary phasing maneuvers on the target orbital pattern to achieve the desired orbital configuration are avoided. Moreover, a novel idea is presented to optimize the flight of satellites, which plays an important role in complying with the constraint of overall fuel cost minimization as much as possible. In order to achieve the global optimal solution of the satellite constellation reconfiguration problem, the efficient hybrid Particle Swarm Optimization/Genetic Algorithm (PSO/GA) technique, is implemented. Finally, to indicate the superiority of the presented method, a comparison to the simultaneous maneuver viewpoint is made on a number of representative cases. The obtained results imply significant reduction of reconfiguration costs by employing the proposed method.     

H-reversal trajectory: A new mission application for high-performance solar sails

The aim of this paper is to quantify the performance of a flat solar sail to perform a double angular momentum reversal maneuver and produce a new class of two-dimensional, non-Keplerian orbits in the ecliptic plane. For a given pair of orbital parameters, the orbital period and the perihelion distance, it is possible to find the minimum solar sail characteristic acceleration required to fulfil a double angular momentum reversal trajectory. This problem is addressed using an optimal formulation and is solved through an indirect approach. The new trajectories are symmetrical with respect to the sun-perihelion line and exhibit a bean-like shape. Two main difficulties must be properly taken into account. On one side the sail is required to perform a rapid reorientation maneuver when it approaches the perihelion. Suitable simulations have shown that such a maneuver is feasible. In the second place the new trajectories require the use of high performance solar sails. For example, assuming an orbital period equal to 5 years, the required solar sail characteristic acceleration is greater than 3.4 mm/s². Such a value, although beyond the currently available sail performance, is comparable to what is required by the original concept of H-reversal maneuvers introduced by Vulpetti in 1996.     

Applying fuzzy bi-dimensional scenario-based model to the assessment of Mars mission architecture scenarios

Sending man to Mars has been a long-held dream of humankind. NASA plans human planetary explorations using approaches that are technically feasible, have reasonable risks and have relatively low costs. This study presents a novel Multi-Attribute Decision Making (MADM) model for evaluating a range of potential mission scenarios for the human exploration of Mars. The three alternatives identified by the Mission Operations Directorate (MOD) at the Johnson Space Center (JSC) include split mission, combo lander and dual scenarios. The proposed framework subsumes the following key methods: first, the conjunction method is used to minimize the number of alternative mission scenarios; second, the Fuzzy Risk Failure Mode and Effects Analysis (RFMEA) is used to analyze the potential failure of the alternative scenarios; third, the fuzzy group Real Option Analysis (ROA) is used to estimate the expected costs and benefits of the alternative scenarios; and fourth, the fuzzy group permutation approach is used to select the optimal mission scenario. We present the results of a case study at NASA’s Johnson Space center to demonstrate: (1) the complexity of mission scenario selection involving subjective and objective judgments provided by multiple space exploration experts; and (2) a systematic and structured method for aggregating quantitative and qualitative data concerning a large number of competing and conflicting mission events.     

加速度计温控系统被控对象建模技术研究

加速度计工作过程中,其内部与外界环境存在温度梯度,针对温度变化影响加速度计参数的问题,提出了一种加速度计温控系统被控对象建模的方法。进行了多工作点加速度计升温和降温试验,采用递推增广最小二乘法对被控对象模型进行了辨识,建立了加速度计温控系统被控对象模型和降温模型,计算结果表明,3min后温控系统被控对象模型曲线残差在-0.5~0.5℃之间,降温模型曲线残差在-0.15~0.15℃之间,满足系统对加速度计性能需求,表明了方法的有效性。