飞行器栖落机动轨迹可达域分析

飞行器的栖落机动被认为是解决舰载固定翼飞机超短距精确着舰的一种可行方式,已有研究主要集中于栖落过程大迎角气动力建模、标称栖落轨迹优化与轨迹跟踪控制,而鲜有对栖落着舰任务的总体分析。基于飞行器纵向栖落机动动力学模型,引入栖落轨迹可达域的概念,并给出可达域的数学描述;在此基础上,对可达域求解采用计算速度和精度兼具的高斯伪谱法,将可达域问题拆分为三个轨迹优化问题依次求解,首先求解栖落轨迹终端高度上边界,然后求解终端高度一定时的最近轨迹,最后求解终端高度一定时的最远轨迹,并给出相应的轨迹优化数学模型。结果表明：运用该模型及求解方法能快速得到计算结果,无动力飞行器的纵向栖落轨迹可达域为上窄下宽的不对称区域,且左右边界曲线线性度较好。     

Optimal robust linearized impulsive rendezvous

The optimization of time-fixed linearized impulsive rendezvous with control uncertainty is investigated. One performance index related to the variances of the terminal state error is defined as the performance index of robustness which is calculated by linear covariance method. The two-objective optimization problem of minimizing the total characteristic velocity and the performance index of robustness is formulated based on the Clohessy–Wiltshire (C-W) system and solved by the nondominated sorting genetic algorithm. The Pareto-optimal solution sets of one homing rendezvous mission are provided and the Pareto optimality is verified by comparing with the fuel-optimal and the robustness-optimal solutions. It is shown that the proposed approach can quickly investigate the relation between the fuel cost and the trajectory robustness, besides evaluate different rendezvous maneuver schemes.     

导弹靶弹弹道机动变轨通用控制指令设计

导弹进行一定强度的机动飞行,就能够提高突防能力,因而,在军事训练中需要具有末端机动变轨能力的靶弹.针对靶弹机动变轨弹道的设计问题,提出变轨通用控制指令设计形式,利用此设计可使靶弹位移与过载指令控制信号协调配合,控制靶弹实现跃升机动、蛇行机动、摆式机动和螺旋机动等变轨形式.通过仿真证明,此设计的变轨弹道通用控制指令,可以满足靶弹跃升机动、蛇行机动、摆式机动和螺旋机动等各种变轨形式的弹道设计要求.     

变质心再入弹头螺旋机动突防弹道设计

为提高高超声速变质心再入弹头的突防能力,设计了一种基于主动螺旋机动的突防弹道。在建立变质心弹头动力学模型和弹目运动学模型的基础上,引入新的控制变量,通过控制质量块的偏移位置,将速度矢量和弹目连线的夹角控制在固定值并且使速度矢量绕弹目连线以一定转速旋转来实现螺旋机动。在实现螺旋机动的基础上,推导了弹体处于螺旋机动状态下的螺旋机动半径和稳态脱靶量均方根的解析解。六自由度弹道仿真表明,所设计的突防方案使再入弹头能够进行有效的螺旋机动,通过合理控制机动幅度大小,可以在实现螺旋机动突防的同时保证较高的命中精度。     

基于高斯伪谱法的吸气式高超声速飞行器爬升弹道优化研究

吸气式高超声速飞行器爬升弹道设计是其总体设计的一个重要问题。这里提出一种基于高斯伪谱法的弹道优化方法,用于解决该问题。以末端弹道倾角为性能指标,飞行攻角为设计变量,建立了飞行器纵平面弹道优化模型。通过高斯伪谱法对状态变量和控制变量进行离散,将最优控制问题转化为非线性规划问题,再利用序列二次规划算法对其进一步求解。仿真结果表明,该方法收敛域大,对初值不敏感,设计精度高,是吸气式高超声速飞行器方案弹道优化的重要方法。     

基于Proximal-Newton-Kantorovich凸规划的空天飞行器实时轨迹优化

针对空天飞行器大气层内上升段实时轨迹优化问题，提出一种基于Proximal-Newton-Kantorovich凸规划的轨迹优化方法。首先，应用Newton-Kantorovich迭代方法将轨迹优化问题转化为一系列的子问题，每个子问题都是一个线性最优控制问题；其次，针对Newton-Kantorovich迭代方法忽略运动方程中的高阶信息，导致难以收敛这一问题，提出Proximal-Newton-Kantorovich迭代方法，在子问题的性能指标中加入邻近规则化项，改善了Newton-Kantorovich迭代方法的收敛性；最后，将子问题离散为二阶锥规划问题，并应用内点法进行求解。提出的Proximal-Newton-Kantorovich凸规划方法是一种求解非线性轨迹规划问题的可行途径。理论分析表明，Proximal-Newton-Kantorovich迭代方法的收敛结果一定是轨迹优化问题的局部最优解。数值实验表明，此方法的计算时间在毫秒级。     

多飞行器再入段时间协同弹道规划方法

提出了一种多飞行器再入段时间协同弹道规划方法。首先,在纵向平面内规划满足航程与终端约束的纵向标称轨迹。随后,在采用轨迹跟踪律跟踪纵向标称轨迹的同时,运用考虑初始横侧向状态的多边界航向偏差角走廊策略控制飞行器的横侧向机动,以满足到达时间约束与终端约束,进而实现单枚飞行器到达时间约束下的轨迹规划。在此基础上,完成了飞行器的到达时间分布与飞行能力分析,给出了最小与最大到达时间的分析计算方法,并根据多飞行器协同再入的任务需求完成了协同飞行时间决策。最后,多飞行器协同再入与扰动条件下的仿真结果表明,该方法能够规划出满足到达时间与终端约束的协同再入轨迹,具备良好的计算精度与鲁棒性。     

An online ensemble semi-supervised classification framework for air combat target maneuver recognition

Online target maneuver recognition is an important prerequisite for air combat situation recognition and maneuver decision-making. Conventional target maneuver recognition methods adopt mainly supervised learning methods and assume that many sample labels are available. However, in real-world applications, manual sample labeling is often time-consuming and laborious. In addition, airborne sensors collecting target maneuver trajectory information in data streams often cannot process information in real time. To solve these problems, in this paper, an air combat target maneuver recognition model based on an online ensemble semi-supervised classification framework based on online learning, ensemble learning, semi-supervised learning, and Tri-training algorithm, abbreviated as Online Ensemble Semi-supervised Classification Framework (OESCF), is proposed. The framework is divided into four parts: basic classifier offline training stage, online recognition model initialization stage, target maneuver online recognition stage, and online model update stage. Firstly, based on the improved Tri-training algorithm and the fusion decision filtering strategy combined with disagreement, basic classifiers are trained offline by making full use of labeled and unlabeled sample data. Secondly, the dynamic density clustering algorithm of the target maneuver is performed, statistical information of each cluster is calculated, and a set of micro-clusters is obtained to initialize the online recognition model. Thirdly, the ensemble K-Nearest Neighbor (KNN)-based learning method is used to recognize the incoming target maneuver trajectory instances. Finally, to further improve the accuracy and adaptability of the model under the condition of high dynamic air combat, the parameters of the model are updated online using error-driven representation learning, exponential decay function and basic classifier obtained in the offline training stage. The experimental results on several University of California Irvine (UCI) datasets and real air combat target maneuver trajectory data validate the effectiveness of the proposed method in comparison with other semi-supervised models and supervised models, and the results show that the proposed model achieves higher classification accuracy.     

Optimization of observer trajectories for bearings-only targetlocalization

The problem of bearings-only target localization is to estimate the location of a fixed target from a sequence of noisy bearing measurements. Although, in theory, this process is observable even without an observer maneuver, estimation performance (i.e., accuracy, stability and convergence rate) can be greatly enhanced by properly exploiting observer motion to increase observability. This work addresses the optimization of observer trajectories for bearings-only fixed-target localization. The approach presented herein is based on maximizing the determinant of the Fisher information matrix (FIM), subject to state constraints imposed on the observer trajectory (e.g., by the target defense system). Direct optimal control numerical schemes, including the recently introduced differential inclusion (DI) method, are used to solve the resulting optimal control problem. Computer simulations, utilizing the familiar Stansfield and maximum likelihood (ML) estimators, demonstrate the enhancement to target position estimability using the optimal observer trajectories     

基于分解策略的SSO发射轨道遗传全局优化设计

提出了基于轨道分解优化和遗传算法（GA）的SSO发射轨道优化设计策略。针对多个轨道段相互耦合问题,基于分解优化策略,将整个发射轨道设计问题分解为两个轨道段设计问题。为了高效可靠地获得全局最优解,对基本遗传算法进行了改进。首先提出了基于多变异操作等改进措施的改进遗传算法;此外,结合遗传算法的全局搜索特性和Powell算法的局部搜索特性,设计了一种串行混合遗传算法。一个二级SSO运载火箭的计算结果表明,轨道分解优化策略确保了问题的成功求解,改进遗传算法和混合遗传算法均可稳定地获得全局最优解,但是混合算法更有效地提高了GA性能。     

Optimal Aircraft Go?Around and Flare Maneuvers

This paper analyzes in detail two of the critical aircraft maneuvers associated with approach and landing: the go-around maneuver and the flare maneuver. Optimal solutions that include state and control variable constraints are obtained for both problems. Two algorithms are given for computation of the minimum and maximum altitude loss associated with the pilot-controlled go-around maneuver. A matrix operator is obtained that can be used for in-flight computation of the altitude loss on a small general-purpose digital computer. The flare optimization presented is for a cost functional that includes both the longitudinal touchdown dispersion and the normal acceleration. A closed-loop mechanization is given that approximates the optimal trajectory. A second matrix operator which can be used for prediction of the longitudinal touchdown point is obtained. Uncertainties are also obtained for the purpose of establishing a prediction confidence level. It is proposed that these prediction techniques should be incorporated into a decision making performance monitor. This monitor could provide the pilot with a continuous assessment of the approach and could generate a preflare decision on whether or not to commit the aircraft to the flare maneuver.     

基于任务安全性的亚轨道飞行器返回轨迹优化

基于高斯伪谱法,结合亚轨道飞行器返回段特点,从任务安全性的角度出发进行了返回轨迹优化研究。出于实际控制能力及安全性的考虑,采用伪控制量作为最优控制变量,摒弃了再入分段、末端区域能量管理段、航向校正圆锥等概念,引入"末端进场走廊"来描述性能指标及终端约束。仿真结果表明,在满足各种约束条件下,能够快速准确地生成亚轨道飞行器返回轨迹,同时验证了结果的可行性与最优性。     

样条节点优化方法及其在弹道表示中的应用

精确的主动段弹道表示模型是高精度弹道参数估计算法的关键。针对等距节点样条模型表示主动段弹道截断误差过大问题,引入一种基于残差的自适应样条节点优化方法,用自由节点样条表示主动段弹道,并根据假设检验理论给出算法中的阈值确定方法。仿真结果表明,经该方法进行节点优化后的样条模型,不但能有效控制模型误差,当检验水平较高时,还能初步识别导弹主动段特征点。     

星际小推力转移轨道快速设计方法

 针对星际探测任务中燃料最省小推力转移轨道问题,提出一种基于标称轨道的快速设计方法。首先以具有相同端点时刻的无摄动标称轨道为参考,对传统的非线性轨道优化模型进行合理变换,将复杂的优化问题简化为可解的两点边值问题;然后基于标称轨道3个独立积分推导出解析的状态转移矩阵,并以此为基础导出了两点边值问题的最优解析解。该方法无需数值迭代,有效地克服了数值优化方法收敛性差、计算效率低的缺点。最后,以探测火星的小推力转移轨道为例对该方法进行了验证,与精确的数值结果相比,该方法计算的燃料消耗误差小于1%。     

基于能量优化的无人机机动轨迹生成方法

基于能量优化的无人机机动能够使无人机在不失去决定性位置优势的情况下获取相对于对手的能量优势，足够的能量优势可转换为有效的位置优势，有利于无人机在空战中随时改出当前机动并投入下一机动动作，对于无人机获取空战胜利至关重要。开展了基于能量优化的无人机机动轨迹生成方法研究，通过在无人机机动飞行包线内设计合适的机动指令，使得无人机能量性能指标最优。以上升转弯机动为例进行了机动轨迹生成的详细设计，并与常规上升转弯机动轨迹生成结果进行对比，仿真结果显示所设计的机动动作完成时间缩短了28.6%~83.8%，总能量变化减少了64.7%~70.1%，实现了无人机机动飞行的能量优化。     

基于凸优化的再入轨迹三维剖面规划方法

可重复使用飞行器一般采用大升阻比气动外形，再入轨迹三维剖面规划方法可充分发挥这类飞行器固有的机动能力。计算量大是制约三维剖面规划应用的难题，为提高计算效率，提出了一种基于凸优化的再入轨迹三维剖面规划方法。首先，分析运动方程特性，利用定义新的控制变量、约束松弛、连续线性化等技术，将原始非凸的三维剖面规划问题转化为一个凸优化问题。其次，将指令反解步骤嵌入至序列凸化算法中，通过迭代求解凸优化子问题，获得原问题的可行解。数值仿真结果表明所提方法具有较高的求解精度和确定的收敛性质，飞行器的机动能力得到充分发挥；与伪谱法的结果对比表明凸优化方法在轨迹规划问题上具有更高的求解效率。     

考虑禁飞圆的滑翔式机动弹道与气动特性参数耦合设计

 为获得滑翔式再入飞行器最佳气动与弹道机动性能,针对规避禁飞圆的远程滑翔式再入问题提出了一种机动弹道与气动特性参数耦合设计方法。耦合设计外环以气动特性参数为设计变量,基于抛物阻力极线模型提取最大升阻比和对应升力系数为气动特性参数;耦合设计内环以泛化升力系数和侧倾角为设计变量,获得给定升阻特性下能规避禁飞圆且满足再入走廊要求的滑翔式再入轨迹。耦合设计问题以再入驻点总热流最小为优化目标,以再入走廊、终端位置和速度为约束,求解满足弹道机动要求且目标函数最小的最佳气动特性参数。提出了一种规避禁飞圆的侧向几何制导逻辑用于内环轨迹设计。仿真算例得出禁飞圆半径越大,需要的滑翔式再入飞行器最大升阻比越大,且再入轨迹刚好能绕过禁飞圆。仿真结果验证了耦合设计方法和侧向制导逻辑的有效性,该方法可为飞行器方案设计时的气动布局选型等工作提供参考。     

基于混合规划策略的自由漂浮冗余机械臂减振运动规划(英文)

对自由漂浮柔性冗余机械臂系统的减振运动规划问题进行研究。首先,采用拉格朗日方法建立系统的动力学模型,并给出了减振运动规划的模型。其次,提出一种基于高斯伪谱法与直接打靶法的混合规划策略,用于求解运动规划问题。先利用高斯伪谱法将运动规划问题转化为一个非线性参数优化问题,并用遗传算法确定近似解,随后利用直接打靶法建立优化模型,将得到的近似解作为优化变量的初值,并采用序列二次规划算法求得精确解。最后,对算例进行分析,验证了基于混合规划策略得到的关节运动轨迹能够明显减小柔性臂杆的全局振动以及残余振动。数值计算的结果表明该方法对减振运动规划问题的求解是有效且稳定的。     

High-precision shape approximation low-thrust trajectory optimization method satisfying bi-objective index

The shape approximation method has been proven to be rapid and practicable in resolving low-thrust trajectory; however, it still faces the challenges of large deviation from the optimal solution and inability to satisfy the specific flight time and fuel mass constraints. In this paper, a modified shape approximation low-thrust model is presented, and a novel constrained optimization algorithm is developed to solve this problem. The proposed method aims at settling the bi-objective optimization o...     

基于二代小波的轨迹优化节点自适应加密

针对采用直接法求解轨迹优化问题中精度和效率之间的矛盾,提出了基于二代小波轨迹优化节点自适应加密.采用RK(Runge-Kutta)离散方法将原轨迹优化问题转化为非线性规划问题,并采用成熟的非线性规划算法求解.对控制或状态函数进行小波变换得到小波系数,基于小波系数和二分节点的对应关系,根据小波系数的幅值确定下一个迭代步所使用的节点并进行序列优化.算例结果表明:通过设置合适的小波系数阀值,采用较少的时间离散节点即可使优化结果达到预定的精度.与高斯伪谱法软件相比,节点个数大约减少10%,最优指标的精度大约提高1个数量级.