运载火箭遥测数据智能判读算法应用

在运载火箭高发射密度、高判读需求、高数据量的背景下，现有自动化判读的判据覆盖率不全、判据编写门槛高、耗时多的问题日益凸显，缺少较通用的算法对传统判读算法未覆盖的判读任务进行判读补充，进而影响运载火箭效果评估与系统性能评定。为充分挖掘海量遥测数据中隐含的参数变化规律，设计智能判读算法作为传统算法的有益补充，提升传统判读的判读覆盖率和判读效率。以液体运载火箭长期加电试验产生的遥测数据为研究对象，设计集成神经网络智能判读算法，在给出的判读指标下研究得出，集成神经网络在频率异常、丢帧等五种现有判据难以描述的判读场景下，判读性能提升30%，提高了现有判据的覆盖率，后续可为判读体系完善和智能判读落地提供研究参考。     

一种面向空天飞机再入的智能自适应复合控制方法

针对空天飞机再入横、侧向通道的姿态控制问题，设计了一种智能神经网络自适应复合控制方法，基于误差反馈学习准则在线更新神经网络权重以补偿全量姿态控制律输出的姿态控制指令。同时，面向再入过程横侧通道的强耦合问题，引入了耦合控制系数，以降低横、侧通道间的控制干扰。此外，提出了一种自适应链式控制分配律，在控制信号中引入正交优化多正弦激励，基于递推最小二乘方法对气动参数进行在线辨识，进而实时更新链式分配策略。最后，对空天飞机再入横侧向通道的神经网络自适应复合控制方法进行数学仿真校验，验证了该方法的有效性和鲁棒性。     

Optimal trajectory design accounting for the stabilization of linear time-varying error dynamics

This study is dedicated to the development of a direct optimal control-based algorithm for trajectory optimization problems that accounts for the closed-loop stability of the trajectory tracking error dynamics already during the optimization. Consequently, the trajectory is designed such that the Linear Time-Varying(LTV) dynamic system, describing the controller’s error dynamics, is stable, while additionally the desired optimality criterion is optimized and all enforced constraints on the traje...     

飞行器制导控制一体化编队控制器设计

针对主从式结构飞行器协同编队控制问题，以侧滑转弯飞行器为研究对象，采用制导控制一体化(Integrated guidance and control, IGC)方法设计编队控制器。首先在惯性坐标系中定义相对运动坐标系，建立相对运动模型，结合飞行器动力学模型，得到全状态制导控制一体化模型；然后采用反演方法，结合滑模变结构与神经网络自适应理论设计了编队控制器，并证明了控制系统稳定性；最后在高速情况下进行了六自由度数值仿真，对比了IGC设计方法与分离设计方法的控制性能。仿真结果表明所设计的IGC控制器能够快速精确地对期望编队队形进行构建与保持，并且较分离设计方法具有优越性。     

动态负调约束下固体冲压发动机燃气流量控制算法研究

变流量固体冲压发动机工作过程中，常采用控制燃气发生器压强的方式调节燃气生成量，在跟踪变化压强指令过程中，流量会产生明显的负调现象，严重影响发动机系统的安全性和可靠性。为 解决这一问题，本文以具有高度非线性、强时变特性的燃气发生器系统为对象，构建了基于粒子群算法的流量调节自适应PI控制器，并给出一种利用自适应微分跟踪器对期望控制指令进行“整形”的负调改善算法；首先，结合负调发生、终止判据，综合考量系统流量安全裕度，负调发生方向，构建了适配于动态负调约束的自适应跟踪微分器（TD），克服了传统跟踪微分器在负调抑制过程中控制精度不高，指向性不强等缺陷；进一步，开展了多工况下的负调抑制算法仿真校验，结果表明：所设计自适应控制算法具备良好的燃气流量控制能力，可实现在基本不改变响应时间情况下，将最大负调量削减56.6%的控制效果，展现出良好的工程应用前景。     

面向火箭构型论证的运载能力快速分析方法

针对不同构型与任务条件下的运载能力快速计算问题，提出了基于高斯函数和组合神经网络的速度损失计算方法，并基于此对运载能力进行了快速分析。首先，基于状态量解析解计算分析，采用高斯函数对核心的重力速度损失项进行拟合计算；同时，为提高多构型与多任务样本的采样密度、简化数据建模过程并增强方法适应性，采用径向基网络(RBF)与深度神经网络(DNN)的组合形式进行状态量的提取与回归分析；然后将任务约束转化为需要速度增量，通过数值迭代得到运载能力。仿真结果表明，此运载能力分析方法精度偏差约为0.35%，计算耗时小于2 s，可为运载火箭总体参数快速论证与任务规划研究提供理论支撑。     

Surrogate role of machine learning in motor-drive optimization for more-electric aircraft applications

Motor drives form an essential part of the electric compressors, pumps, braking and actuation systems in the More-Electric Aircraft (MEA). In this paper, the application of Machine Learning (ML) in motor-drive design and optimization process is investigated. The general idea of using ML is to train surrogate models for the optimization. This training process is based on sample data collected from detailed simulation or experiment of motor drives. However, the Surrogate Role (SR) of ML may vary for different applications. This paper first introduces the principles of ML and then proposes two SRs (direct mapping approach and correction approach) of the ML in a motor-drive optimization process. Two different cases are given for the method comparison and validation of ML SRs. The first case is using the sample data from experiments to train the ML surrogate models. For the second case, the joint-simulation data is utilized for a multi-objective motor-drive optimization problem. It is found that both surrogate roles of ML can provide a good mapping model for the cases and in the second case, three feasible design schemes of ML are proposed and validated for the two SRs. Regarding the time consumption in optimizaiton, the proposed ML models can give one motor-drive design point up to 0.044 s while it takes more than 1.5 mins for the used simulation-based models.     

Quick identification of guidance law for an incoming missile using multiple-model mechanism

A guidance law parameter identification model based on Gated Recurrent Unit (GRU) neural network is established. The scenario of the model is that an incoming missile (called missile) attacks a target aircraft (called aircraft) using Proportional Navigation (PN) guidance law. The parameter identification is viewed as a regression problem in this paper rather than a classification problem, which means the assumption that the parameter is in a finite set of possible results is discarded. To increase the training speed of the neural network and obtain the nonlinear mapping relationship between kinematic information and the guidance law parameter of the incoming missile, an output processing method called Multiple-Model Mechanism (MMM) is proposed. Compared with a conventional GRU neural network, the model established in this paper can deal with data of any length through an encoding layer in front of the input layer. The effectiveness of the proposed Multiple-Model Mechanism and the performance of the guidance law parameter identification model are demonstrated using numerical simulation.