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为提高无人机着陆效率,从着陆速度向量场和导引律设计两方面研究改进。首先,基于椭圆设计速度向量场,实现飞行路程更短、机动性能要求更低的着陆轨迹。然后,基于像素坐标系与机体坐标系的关系,设计无人机的航迹方位角指令;以椭圆切线方向为参考,结合合作矢量特征,设计航迹倾斜角指令;利用图像信息,设计速度大小指令。最后,理论比较了传统轨迹与提出轨迹对方向机动性性能的要求,给出了轨迹参数与无人机方向机动性性能的关系。利用Simulink搭建系统仿真平台,计算满足要求的合作矢量特征。结果表明,无人机以曲线轨迹准确软着陆到目标,满足实际运用的需要。 相似文献
283.
针对云制造环境下机床装备资源具有数量大、异质异构、服务制约因素多等问题,提出了基于多准则决策的机床装备资源选择方法。首先,利用本体论对机床装备资源建模,将物理资源虚拟化封装,实现虚拟资源与物理资源的逻辑映射。进一步地,使用语言变量量化决策者评估信息,采用模糊决策与试验评价实验室(FDEMATEL)和熵值法(EW)相结合的组合赋权法,引入模糊VIKOR(FVIKOR)多属性决策方法对机床装备资源进行综合评估,建立FDEMATEL-EW-FVIKOR的多准则混合决策模型。最后,将该方法应用于某生产制造企业,实验对比决策结果验证了多准则混合决策模型的可行性和有效性。 相似文献
284.
Roberto Furfaro Riccardo Barocco Richard Linares Francesco Topputo Vishnu Reddy Jules Simo Lucille Le Corre 《Advances in Space Research (includes Cospar's Information Bulletin, Space Research Today)》2021,67(1):617-638
Close proximity operations around small bodies are extremely challenging due to their uncertain dynamical environment. Autonomous guidance and navigation around small bodies require fast and accurate modeling of the gravitational field for potential on-board computation. In this paper, we investigate a model-based, data-driven approach to compute and predict the gravitational acceleration around irregular small bodies. More specifically, we employ Extreme Learning Machine (ELM) theories to design, train and validate Single-Layer Feedforward Networks (SLFN) capable of learning the relationship between the spacecraft position and the gravitational acceleration. ELM-base neural networks are trained without iterative tuning therefore dramatically reducing the training time. Analysis of performance in constant density models for asteroid 25143 Itokawa and comet 67/P Churyumov-Gerasimenko show that ELM-based SLFN are able learn the desired functional relationship both globally and in selected localized areas near the surface. The latter results in a robust neural algorithm for on-board, real-time calculation of the gravity field needed for guidance and control in close-proximity operations near the asteroid surface. 相似文献
285.
Yeerang Lim Youeyun Jung Hyochoong Bang 《Advances in Space Research (includes Cospar's Information Bulletin, Space Research Today)》2018,61(10):2661-2672
This study presents model predictive formation control based on an eccentricity/inclination vector separation strategy. Alternative collision avoidance can be accomplished by using eccentricity/inclination vectors and adding a simple goal function term for optimization process. Real-time control is also achievable with model predictive controller based on convex formulation. Constraint-tightening approach is address as well improve robustness of the controller, and simulation results are presented to verify performance enhancement for the proposed approach. 相似文献
286.
为满足某飞行器推力矢量试验的测试要求,采用两台六分量应变天平和一个空气桥组成测力装置来实现飞机全机气动力和喷管推进特性同时分别测量。基于有限元软件,对天平的应变及空气桥对天平的干扰进行了分析。结果表明:空气桥对天平力分量的干扰值优于5%,对力矩(滚转力矩除外)分量的干扰值优于15%,达到了设计指标。通过校准获得了单独天平、天平带空气桥(充压、不充压)状态下的校准公式,校准结果表明:两台天平各分量的综合加载误差均优于0.3%,天平带空气桥(充压、不充压)状态下各分量的综合加载误差优于0.5%,空气桥对天平的干扰量值与有限元分析结果一致。理论分析与实测结果证明:研制的天平及空气桥达到了预定目标,它的测量精准度高,满足推力矢量风洞试验需求。 相似文献
287.
Roddy M Grieves Éléonore Duvelle Paul A Dudchenko 《Spatial Cognition & Computation》2018,18(3):217-256
Hippocampal place cells are thought to form the neural substrate of a global cognitive map. However, in multicompartment mazes, these cells exhibit locally repeating representations, undermining the global cognitive map view of place cells. This phenomenon appears to be related to the repetitive layout of these mazes, but still no hypothesis adequately explains it. Here, we use a boundary vector cell (BVC) model of place cell firing to model the activity of place cells in numerous multicompartment environments. The activity of modeled place cells bears a striking resemblance to experimental data, replicating virtually every major experimental result. Our results support the BVC model and indicate that locally repeating place cell firing could result purely from local geometry. 相似文献
288.
Mansour Kabganian Hamed Kouhi Morteza Shahravi Farhad Fani Saberi 《Advances in Space Research (includes Cospar's Information Bulletin, Space Research Today)》2018,61(9):2328-2343
The thrust vector control (TVC) scheme is a powerful method in spacecraft attitude control. Since the control of a small spacecraft is being studied here, a solid rocket motor (SRM) should be used instead of a liquid propellant motor. Among the TVC methods, gimbaled-TVC as an efficient method is employed in this paper. The spacecraft structure is composed of a body and a gimbaled-SRM where common attitude control systems such as reaction control system (RCS) and spin-stabilization are not presented. A nonlinear two-body model is considered for the characterization of the gimbaled-thruster spacecraft where, the only control input is provided by a gimbal actuator. The attitude of the spacecraft is affected by a large exogenous disturbance torque which is generated by a thrust vector misalignment from the center of mass (C.M). A linear control law is designed to stabilize the spacecraft attitude while rejecting the mentioned disturbance torque. A semi-analytical formulation of the region of attraction (RoA) is developed to ensure the local stability and fast convergence of the nonlinear closed-loop system. Simulation results of the 3D maneuvers are included to show the applicability of this method for use in a small spacecraft. 相似文献
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