进化神经网络在复合材料格栅结构设计中的应用

根据Kolmogorov多层神经网络映射存在定理,利用进化神经网络来实现结构设计参数(输入)与结构响应参数(输出)的全局非线性映射关系,以此来代替实际结构优化过程中存在的大量有限元计算,从而提高优化效率。以遗传算法为优化求解器,神经网络屈曲稳定性响应面为主要约束,对复合材料格栅加筋结构的优化问题进行了分析研究。算例表明,在相同(有限元)样本数据的情况下,进化神经网络通过自适应调节网络结构和权值,可获得比BP神经网络更高精度的映射模型,具有很强的泛化能力。该方法可为解决大型复合材料结构优化设计提供一条高效途径。     

小型固体运载器起飞段姿态控制方法研究

针对控制系统,采用侧喷流发动机和栅格舵的新型小型多级固体运载火箭开展了起飞段姿态控制方法研究。给出了在低马赫数条件下侧喷流发动机的力放大因子和栅格舵的控制力矩,建立了运载器姿态控制模型。根据侧喷流和栅格舵的特点,设计了PID控制器与智能开关控制器相结合的新型控制器,运用神经网络算法实现了开关控制器参数的在线选择。仿真结果表明,所设计的控制器满足控制精度、稳定度和鲁棒性的要求,算法简便,具有工程可实现性,以侧喷流发动机和栅格舵为执行机构的控制系统能满足运载火箭起飞段姿态控制要求,为陆基或空射小型固体运载火箭姿态控制方法提供了一种新思路。     

Model-free adaptive optimal design for trajectory tracking control of rocket-powered vehicle

An adaptive optimal trajectory tracking controller is presented for the Solid-Rocket-Powered Vehicle (SRPV) with uncertain nonlinear non-affine dynamics in the framework of adaptive dynamic programming. First, considering that the ascent model of the SRPV is non-affine, a model-free Single Network Adaptive Critic (SNAC) method is developed based on the dynamic neural network and the traditional SNAC method. This developed model-free SNAC method overcomes the limitation of the traditional SNAC method that can only be applied to affine systems. Then, a closed-form adaptive optimal controller is designed for the non-affine dynamics of SRPVs. This controller can adjust its parameters under different flight conditions and converge to the approximate optimal controller through online self-learning. Finally, the convergence to the approximate optimal controller is proved. The theoretical analysis of the uniformly ultimate boundedness of the tracking error is also presented. Simulation results demonstrate the effectiveness of the proposed controller.     

Modeling and Algorithm Application of Weapon Assignment System

In order to improve weapon assignment(WA)accuracy in real scenario,an artificial neural network(ANN)model is built to calculate real-time weapon kill probabilities.Considering the WA characteristic,each input representing one assessment index should be normalized properly.Therefore,the modified WA model is oriented from constant value to dynamic computation.Then an improved invasive weed optimization algorithm is applied to solve the WA problem.During search process,local search is used to improve the initial population,and seed reproduction is redefined to guarantee the mutation from multipoint to single point.In addition,the idea of vaccination and immune selection in biology is added into optimization process.Finally,simulation results verify the model′s rationality and effectiveness of the proposed algorithm.     

一种弹道修正弹SINS任意滚转角快速粗对准方法

针对弹道修正弹药出炮口后滚转角处于随机状态,捷联惯导系统(SINS)失准角过大时卡尔曼滤波收敛困难的问题,提出在卫星拒止环境下利用神经网络快速估计初始滚转角的改进方法。在炮口处布设少量导航信标,建立反向传播(BP)神经网络拟合初始滚转角与观测量间的非线性映射模型。针对信标辅助下姿态弱可观的问题,引入惯导测量参数作为输入,提高网络估计精度。采用主成分分析法进行特征提取,简化网络结构。仿真结果表明,与基于非线性卡尔曼滤波的对准方法相比,本算法可实现任意滚转角下的快速粗对准;对射角、初始俯仰角误差未在训练范围内以及存在布设误差等场景也进行了测试,与未优化的BP网络相比,对准精度更高,鲁棒性更好。     

基于ANFIS的舵阻横摇系统

在MATLAB环境下建立了舵阻摇系统的非线性模型．基于所建立的非线性模型和ANFIS控制算法进行了仿真实验。运用Simulink工具箱得到仿真曲线．通过仿真曲线可以看出此控制器具有良好的鲁棒性能．通过对比减摇前后的仿真曲线得出,ANFIS算法对于舵阻摇系统的拉制有效,减摇率为23%左右,减摇效果较好。     

On developing data-driven turbulence model for DG solution of RANS

High-order Discontinuous Galerkin(DG) methods have been receiving more and more attentions in the area of Computational Fluid Dynamics(CFD) because of their high-accuracy property. However, it is still a challenge to obtain converged solution rapidly when solving the Reynolds Averaged Navier–Stokes(RANS) equations since the turbulence models significantly increase the nonlinearity of discretization system. The overall goal of this research is to develop an Artificial Neural Networks(ANNs) model with low complexity acting as an algebraic turbulence model to estimate the turbulence eddy viscosity for RANS. The ANN turbulence model is off-line trained using the training data generated by the widely used Spalart–Allmaras(SA) turbulence model before the Optimal Brain Surgeon(OBS) is employed to determine the relevancy of input features.Using the selected relevant features, a fully connected ANN model is constructed. The performance of the developed ANN model is numerically tested in the framework of DG for RANS, where the‘‘DG+ANN" method provides robust and steady convergence compared to the ‘‘DG+SA" method. The results demonstrate the promising potential to develop a general turbulence model based on artificial intelligence in the future given the training data covering a large rang of flow conditions.     

星座备份策略研究进展

针对星座卫星在轨运行时因操作或故障导致星座服务性能下降的现象，综述了星座备份策略研究的进展。首先，概述了导航星座、通信星座的基本情况和备份现状，归纳出不同星座的备份策略。其次，重点阐述了3种不同的星座备份策略模式，即在轨备份、停泊轨道备份及地面备份，给出了3种备份策略模式的特点。在此基础上，分别从卫星故障模式、卫星可靠性、卫星可用性、星座状态概率和星座系统可用性5个方面梳理了国内外星座备份策略关键因素的研究进展。再次，综述了目前星座备份策略分析的主要模型和方法，包括Petri网、马尔可夫链以及存储论。最后，对未来星座备份策略研究的发展方向进行了展望。     

遗传神经网络PID控制技术研究与应用

针对空调控制系统非线性、大滞后、时变性等特点,通过对中央空调总风量控制法的研究,提出了采用遗传算法神经网络PID控制方法,使空调控制系统具有较高的控制精度和良好的动态特性。仿真及实验结果表明该方法有效可行。     

迎角快速校准及误差对控制系统的影响研究

基于迎角传感器数据校准,目前所采用的线性插值算法对于测量区域的数值处理存在精度丢失的问题,采用三次曲线拟合以及神经网络逼近的方法实现对已有测量数据的精确映射,避免了由于二次映射引起的精度损失,同时提高了对于未测量区域的预测能力,保证了控制系统的跟踪性能。将校准方法通过两余度迎角校准系统进行了验证,并将校准系统加入动态逆飞行控制系统进行仿真,结果显示了算法的精确性和可靠性。