基于扩张状态观测器的再入弹头控制系统设计

为了提高非自旋再入弹头的变质心控制系统的鲁棒性,采用滑模控制方法设计了变质心控制系统中的控制器。为了使控制系统设计简单,将弹头俯仰通道和偏航通道之间的耦合作用以及外界干扰综合为汇总不确定量,并采用扩张状态观测器进行估计和补偿。扩张状态观测器引入到滑模控制器中,从而发展了一种基于扩张状态观测器的滑模控制器。对弹头的变质心控制系统进行了仿真分析,仿真结果表明,所设计的控制器能够有效地补偿汇总不确定量的影响,使控制系统输出快速而准确地跟踪上指令信号。     

发动机谐振与箭体模态耦合稳定性及机理研究

针对发动机谐振可能与箭体弹性振动之间发生耦合共振,从而引起姿态控制系统不稳定的问题,首先将发动机谐振特性简化为一个二阶环节,建立了包含发动机谐振方程的火箭姿态动力学模型;然后分析了发动机谐振对箭体模态极点频率和阻尼比的影响,并从理论上分析了出现负阻尼比时发动机和箭体模态间的耦合共振机理,在此基础上计算了导致姿态控制系统...     

Numerical Simulation of 2D Supersonic Magnetohydrodynamic Channel and Study on Hall Effect

In this research effort,numerical simulation of two-dimensional magnetohydrodynamic(MHD) channel is performed and Hall effect is studied.The computational model consists of the Navier-Stokes(N-S) equations coupled with electrical-magnetic source terms,Maxwell equations and the generalized Ohm’s law.Boundary conditions for the electrical potential equation considering Hall effect are derived.To start with,the MHD channel with single-pair electrodes is studied and flow of the electric current is in accordance with physical principle.Then the MHD channel with five-pair electrodes is numerically simulated.The results show that the electrical current concentrates on the downstream of the anode and the upstream of the cathode due to Hall effect,and the flow field becomes asymmetrical.At the current value of the magnetic interaction parameter,the electrical-magnetic force affects the flow remarkably,decreasing the outlet Mach number and increasing the outlet pressure;what’s more,the flow structure in the channel becomes extremely complex.Performances of MHD channels with continual electrodes and segmented electrodes are compared.The results show that performance of the MHD channel with segmented electrodes is better than that with continual electrodes with the increase of Hall parameter.     

Hyper Heuristic Approach for Design and Optimization of Satellite Launch Vehicle

Satellite launch vehicle lies at the cross-road of multiple challenging technologies and its design and optimization present a typical example of multidisciplinary design and optimization(MDO) process.The complexity of problem demands highly effi-cient and effective algorithm that can optimize the design.Hyper heuristic approach(HHA) based on meta-heuristics is applied to the optimization of air launched satellite launch vehicle(ASLV).A non-learning random function(NLRF) is proposed to con-trol low-level meta-heuristics(LLMHs) that increases certainty of global solution,an essential ingredient required in product conceptual design phase of aerospace systems.Comprehensive empirical study is performed to evaluate the performance advan-tages of proposed approach over popular non-gradient based optimization methods.Design of ASLV encompasses aerodynamics,propulsion,structure,stages layout,mass distribution,and trajectory modules connected by multidisciplinary feasible design approach.This approach formulates explicit system-level goals and then forwards the design optimization process entirely over to optimizer.This distinctive approach for launch vehicle system design relieves engineers from tedious,iterative task and en-ables them to improve their component level models.Mass is an impetus on vehicle performance and cost,and so it is considered as the core of vehicle design process.Therefore,gross launch mass is to be minimized in HHA.     

采用移动粒子法对蒸发管结构优化

采用数值模拟方法研究在驻涡燃烧室试验中出现的部分燃油在外环冷却腔中燃烧的现象,分析导致过低的燃烧效率的原因,并通过优化设计对此现象加以解决.通过移动粒子半隐式方法的计算,结果基本还原了燃油回溅现象,查找出蒸发管结构设计中直段长度过短,并对蒸发管直段结构进行优化加长.在结构优化后的计算中,燃油回溅现象已经得到了很好的抑制,计算溅出的粒子由总数的50%降低至10%左右;试验结果得到的温升法燃烧效率也相应的由50%左右上升至80%～90%,从而验证了优化设计的正确性.     

无人机前开伞式降落过程动力学建模与仿真

文章分析了某型无人机降落伞回收阶段的动力学过程,对各工作阶段的无人机和降落伞的组合体进行了数学建模,使用Matlab软件中的Simulink组件搭建了无人机伞降过程的仿真模型,按照真实飞行和回收工况进行了计算,通过仿真结果与试验数据的比较,验证了模型的正确性.     

滑翔飞行器弹道规划与制导方法综述综述

滑翔飞行器以其突出的机动突防能力、高精度打击以及全球快速可达等优势成为当下航空航天领域研究热点。作为控制飞行器可靠执行既定飞行任务的核心部分,弹道规划与制导方法是关注的焦点。概述当前滑翔飞行器弹道规划与制导方法,尤其是基于标准剖面和基于预测校正思想的弹道规划与制导方法的研究现状。进一步,分析当下滑翔飞行器弹道规划与制导方法的研究热点与难点,并结合人工智能,对滑翔飞行器弹道规划与制导方法未来可能的发展趋势进行了展望。     

带落速落角约束的高超声速飞行器俯冲轨迹规划方法

针对高超声速飞行器俯冲段精确打击任务需求,提出了一种能够同时满足落速与落角约束的轨迹规划方法。建立了两段式轨迹规划策略,第一段采用参数化控制剖面调节飞行速度,第二段采用传统偏置比例导引律实现落角控制。将控制剖面的参数设计分解为多参数优化与单参数搜索两个问题：通过离线求解可行初始位置范围最大的多参数优化问题,提高控制剖面对初始偏差的适应性;通过在线求解带罚函数的单参数搜索问题,得到落速偏差最小的俯冲轨迹。结合高超声速飞行器模型,对所提出的俯冲轨迹规划方法进行了仿真。结果表明,该方法能够得到满足落速与落角约束的俯冲轨迹,具有较好的求解效率,且对初始状态偏差具有较强的鲁棒性。     

基于启发式自适应离散差分进化算法的多UCAV协同干扰空战决策

 研究了多无人作战飞机(UCAV)协同干扰空战决策(MUCJAD)问题,在干扰效能评估指标量化方法的基础上为该问题建立了优化模型。为有效求解该模型,提出一种启发式自适应离散差分进化(H-SDDE)算法。在H-SDDE算法中,设计了包含4种候选解产生策略的候选策略池,引入了候选解产生策略及其参数的自适应学习过程。此外,结合实际问题为算法设计了基于威胁度的扩展型整数编码方案、基于威胁度的启发式个体调整操作、基于约束满足的个体修复操作。在12个测试实例上进行了仿真验证,结果表明,H-SDDE算法与其他同类算法相比在求解质量和求解速度上具有明显优势,能够更好地发挥多UCAV协同干扰整体效果。     

Cooperative task assignment of multiple heterogeneous unmanned aerial vehicles using a modified genetic algorithm with multi-type genes

The task assignment problem of multiple heterogeneous unmanned aerial vehicles （UAVs）, concerned with cooperative decision making and control, is studied in this paper. The heterogeneous vehicles have different operational capabilities and kinematic constraints, and carry limited resources （e.g., weapons） onboard. They are designated to perform multiple consecutive tasks cooperatively on multiple ground targets. The problem becomes much more complicated because of these terms of heterogeneity. In order to tackle the challenge, we modify the former genetic algorithm with multi-type genes to stochastically search a best solution. Genes of chromo- somes are different, and they are assorted into several types according to the tasks that must be performed on targets. Different types of genes are processed specifically in the improved genetic operators including initialization, crossover, and mutation. We also present a mirror representation of vehicles to deal with the limited resource constraint. Feasible chromosomes that vehicles could perform tasks using their limited resources under the assignment are created and evolved by genetic operators. The effect of the proposed algorithm is demonstrated in numerical simulations. The results show that it effectively provides good feasible solutions and finds an optimal one.