某大型低温运载火箭无人值守加注发射技术研究

针对提升和保障航天发射人员安全问题,提出了低温运载火箭无人值守加注发射(CLVUFL)的理念和内涵.通过国内外代表性CLVUFL的技术对比,发现国内低温火箭加注发射应用CLVUFL技术存在箭地接口复杂、加注发射时间较长、关键设备可靠性有待提高、智能化自动化程度不高等主要问题.针对某大型低温运载火箭加注发射流程,通过分析...     

空天体系对抗下未来战斗机发展的一些思考

本文从在未来作战体系中对传统意义上的战斗机有无存在的必要性,未来战斗机的主要作战使命,发展思路,主要特征,使用方式等方面对未来战斗机的特征进行了一些概念性研究和描述。在此基础上,对未来变体战斗机、无人战斗机(UCAV)、未来变体UCAV的关键技术等进行了初步的分析。     

Adaptive path planning for unmanned aerial vehicles based on bi-level programming and variable planning time interval

This paper presents an adaptive path planner for unmanned aerial vehicles (UAVs) to adapt a real-time path search procedure to variations and fluctuations of UAVs’ relevant performances, with respect to sensory capability, maneuverability, and flight velocity limit. On the basis of a novel adaptability-involved problem statement, bi-level programming (BLP) and variable planning step techniques are introduced to model the necessary path planning components and then an adaptive path planner is developed for the purpose of adaptation and optimization. Additionally, both probabilistic-risk-based obstacle avoidance and performance limits are described as path search constraints to guarantee path safety and navigability. A discrete-search-based path planning solution, embedded with four optimization strategies, is especially designed for the planner to efficiently generate optimal flight paths in complex operational spaces, within which different surface-to-air missiles (SAMs) are deployed. Simulation results in challenging and stochastic scenarios firstly demonstrate the effectiveness and efficiency of the proposed planner, and then verify its great adaptability and relative stability when planning optimal paths for a UAV with changing or fluctuating performances.     

低空无人飞艇压力控制系统的设计与实现

介绍了一种以C8051F120单片机为核心设计的低空无人飞艇压力控制系统,详细给出了系统的总体架构;着重说明了主要功能模块的电路设计和固件的程序设计.地面测试和飞行试验表明该压力控制系统性能稳定,能满足低空飞艇飞行中压力控制的要求.     

有控飞行力学在无人飞行器研制和使用中的作用

本文从工程实践观点出发，结合无人飞行器，特别是战术导弹和制导兵器的特点，扼要讨论了与此密切相关的有控习行力学研究工作的某些进展情况，文中将有控习行力学问题分为两大类，即典型的飞行力学问题和相关的飞行力学问题，强调了多学科交 对飞行力学研究的重要意义；同时，还指出了有控飞行力学在未来軎与研制和使用中的作用。     

Cascade-type guidance law design for multiple-UAV formation keeping

A procedure to compute guidance commands for controlling the relative geometry of multiple unmanned aerial vehicles (UAVs) in formation flight is proposed. The concepts of branch, global leader, and local leader/follower are used to represent the whole formation geometry. A positive-definite function defined in terms of the formation error is then introduced and the Lyapunov stability theorem is used to obtain the cascade type guidance law. This scheme leads to the synchronized flight of all UAVs while maintaining formation geometry. The results of a high fidelity nonlinear simulation of a reconnaissance and surveillance mission example are presented to show the effectiveness of the proposed guidance law.     

Aircraft robust multidisciplinary design optimization methodology based on fuzzy preference function

This paper presents a Fuzzy Preference Function-based Robust Multidisciplinary Design Optimization (FPF-RMDO) methodology. This method is an effective approach to multidisciplinary systems, which can be used to designer experiences during the design optimization process by fuzzy preference functions. In this study, two optimizations are done for Predator MQ-1 Unmanned Aerial Vehicle (UAV): (A) deterministic optimization and (B) robust optimization. In both problems, minimization of takeoff weight and drag is considered as objective functions, which have been optimized using Non-dominated Sorting Genetic Algorithm (NSGA). In the robust design optimization, cruise altitude and velocity are considered as uncertainties that are modeled by the Monte Carlo Simulation (MCS) method. Aerodynamics, stability and control, mass properties, performance, and center of gravity are used for multidisciplinary analysis. Robust design optimization results show 46% and 42% robustness improvement for takeoff weight and cruise drag relative to optimal design respectively.     

Real-time trajectory planning for UCAV air-to-surface attack using inverse dynamics optimization method and receding horizon control

This paper presents a computationally efficient real-time trajectory planning framework for typical unmanned combat aerial vehicle (UCAV) performing autonomous air-to-surface (A/S) attack. It combines the benefits of inverse dynamics optimization method and receding horizon optimal control technique. Firstly, the ground attack trajectory planning problem is mathematically formulated as a receding horizon optimal control problem (RHC-OCP). In particular, an approximate elliptic launch acceptable region (LAR) model is proposed to model the critical weapon delivery constraints. Secondly, a planning algorithm based on inverse dynamics optimization, which has high computational efficiency and good convergence properties, is developed to solve the RHCOCP in real-time. Thirdly, in order to improve robustness and adaptivity in a dynamic and uncer- tain environment, a two-degree-of-freedom (2-DOF) receding horizon control architecture is introduced and a regular real-time update strategy is proposed as well, and the real-time feedback can be achieved and the not-converged situations can be handled. Finally, numerical simulations demon- strate the efficiency of this framework, and the results also show that the presented technique is well suited for real-time implementation in dynamic and uncertain environment.     

Robustness evaluation method for unmanned aerial vehicle swarms based on complex network theory

Unmanned Aerial Vehicle (UAV) swarms have been foreseen to play an important role in military applications in the future, wherein they will be frequently subjected to different disturbances and destructions such as attacks and equipment faults. Therefore, a sophisticated robustness evaluation mechanism is of considerable importance for the reliable functioning of the UAV swarms. However, their complex characteristics and irregular dynamic evolution make them extremely challenging and uncertain to evaluate the robustness of such a system. In this paper, a complex network theory-based robustness evaluation method for a UAV swarming system is proposed. This method takes into account the dynamic evolution of UAV swarms, including dynamic reconfiguration and information correlation. The paper analyzes and models the aforementioned dynamic evolution and establishes a comprehensive robustness metric and two evaluation strategies. The robustness evaluation method and algorithms considering dynamic reconfiguration and information correlation are developed. Finally, the validity of the proposed method is verified by conducting a case study analysis. The results can further provide some guidance and reference for the robust design, mission planning and decision-making of UAV swarms.     

An aggregate flow based scheduler in multi-task cooperated UAVs network

Unmanned Aerial Vehicles (UAVs) cooperative multi-task system has become the research focus in recent years. However, the existing network frameworks of UAVs are not flexible and efficient enough to deal with the complex multi-task scheduling, because they are not able to perceive the different features. In this paper, a novel cooperated UAVs network framework for multi-task scheduling is proposed. It is a three-layer network including a core layer, an aggregation layer and an execution layer, which enhances the efficiency of multi-task distribution, aggregation and transmission. Furthermore, an AggreGate Flow (AGFlow) based scheduler is dedicatedly designed to maximize the task completion rate, whose key point is to aggregate flows belonging to one task during the multi-task transmission of UAVs network and to allocate priority by calculating the urgency-level of each AGFlow. Simulation results demonstrate that, compared with that of state-of-the-art scheduler, the average task completion rate of AGFlow based scheduler is raised by 0.278.