Application of improved active disturbance rejection control algorithm in tilt quad rotor

The Tilt Quad Rotor (TQR) has complex dynamics characteristics, especially in conversion mode. It is difficult to build the dynamic model of the TQR and the environmental factors have a great influence on it. To solve the problem of control in conversion mode of TQR, this paper carries out the design of the controller based on improved Active Disturbance Rejection Control (ADRC). According to the characteristics of flight in conversion mode, Tracking Differentiator (TD) with explicit model is used to solve the problem of multiple integrals when the system is high-order system. Extended State Observer (ESO) with Radial Basis Function (RBF) neural network is used to estimate and compensate for internal and external uncertainties, and the adaptive sliding mode control in Nonlinear State Error Feedback (NLSEF) is used to improve the response speed of the controller and reduce the parameters which should be tuned. Through the flight control simulation of the TQR, the validity and rationality of the control system are verified.     

直流电机网络控制系统仿真

对直流电机网络闭环控制系统进行仿真分析,根据电机网络闭环控制系统原理建立仿真模型。对具有数据丢包的直流电机网络伺服控制系统输出响应特性进行仿真,比较系统分别采用PI控制及增益,可调PI控制方式时的输出特性。结果表明,相比常规PI控制增益可调PI控制方法在网络环境下具有更好的控制特性。该研究为网络伺服控制技术提供可靠的参考依据。     

Exploiting global information in complex network repair processes

Robustness of complex networks has been studied for decades,with a particular focus on network attack.Research on network repair,on the other hand,has been conducted only very lately,given the even higher complexity and absence of an effective evaluation metric.A recently proposed network repair strategy is self-healing,which aims to repair networks for larger compo nents at a low cost only with local information.In this paper,we discuss the effectiveness and effi ciency of self-healing,which limits network repair to be a multi-objective optimization problem and makes it difficult to measure its optimality.This leads us to a new network repair evaluation metric.Since the time complexity of the computation is very high,we devise a greedy ranking strategy.Evaluations on both real-world and random networks show the effectiveness of our new metric and repair strategy.Our study contributes to optimal network repair algorithms and provides a gold standard for future studies on network repair.     

基于T-S模糊模型的模糊大系统的混合滤波

针对非线性大系统内部过程参数的检测估计,采用连续模糊大系统模型,利用lyapunov定理和分布式处理方法,给出了模糊大系统的H2/H∞混合滤波器设计方法。该方法在保证滤波误差系统全局稳定的同时,具备了H2/H∞性能。通过求解线性矩阵不等式,可以得到滤波器参数。最后,用一个数值例子验证了该方法的正确性和有效性。     

Air route network optimization in fragmented airspace based on cellular automata

Air route network optimization,one of the essential parts of the airspace planning,is an effective way to optimize airspace resources,increase airspace capacity,and alleviate air traffic con gestion.However,little has been done on the optimization of air route network in the fragmented airspace caused by prohibited,restricted,and dangerous areas (PRDs).In this paper,an air route network optimization model is developed with the total operational cost as the objective function while airspace restriction,air route network capacity,and non-straight-line factors (NSLF) are taken as major constraints.A square grid cellular space,Moore neighbors,a fixed boundary,together with a set of rules for solving the route network optimization model are designed based on cellular automata.The empirical traffic of airports with the largest traffic volume in each of the 9 flight information regions in mainland China is collected as the origin-destination (OD) air port pair demands.Based on traffic patterns,the model generates 35 air routes which successfully avoids 144 PRDs.Compared with the current air route network structure,the number of nodes decreases by 41.67％,while the total length of flight segments and air routes drop by 32.03％ and 5.82％ respectively.The NSLF decreases by 5.82％ with changes in the total length of the air route network.More importantly,the total operational cost of the whole network decreases by 6.22％.The computational results show the potential benefits of the model and the advantage of the algorithm.Optimization of air route network can significantly reduce operational cost while ensuring operation safety.     

基于自适应径向基网络的舰船RCS统计特征识别方法

文章采用舰船RCS频域起伏序列的均值、标准差为识别特征向量,利用提出的基于样本密度的自适应径向基网络,进行舰船分类识别研究。自适应径向基网络采用改进的自适应PSO方法估计样本密度最优邻域半径,实现径向基网络中心的自适应选择。改进的自适应PSO方法采用能反映样本聚类特点的BWP指标为适应度评价函数,采用快慢结合的高斯自适应惯性权重调节策略,提高了最优样本密度邻域半径的搜索速度和精度。实验结果表明,自适应径向基网络能自适应获得径向基网络最优识别率对应的RBF中心及其位置分布,减少了对建模人员经验的依赖,提高了反舰导弹对舰船类型的识别分类能力。     

A regional tropospheric model for airborne GPS applications

A regional tropospheric model can be constructed using surveys from GPS ground networks. Using this model the tropospheric delays of a kinematic station within the region can be interpolated. However, such a model is generally not suitable for an airborne platform high above the ground networks. In this paper, a method of constructing a regional tropospheric model for airborne GPS applications is described. First, the kinematic station in the air is projected onto the ground. Then the tropospheric delays at projected point are interpolated from those of the ground networks. Finally, the tropospheric delays at projected point are extended upward to the airborne platform using pressure and temperature gradients and humidity exponential function. For validation of this method, the data of airborne campaign carried out by BKG in cooperation with GFZ and BGR 2008 in the northern Alps are used. The results show that GPS kinematic positioning precision in height component can be improved using this method.     

基于人工神经网络的停机坪安全预警研究

停机坪是机场的重要组成部分,停机坪安全是机场安全关注的重点。影响停机坪安全作业的要素有很多,通过对这些要素进行监控和预警,可以减少停机坪的安全隐患,保证机场正常运行。根据济南遥墙机场的主要数据,运用因子分析法对影响停机坪安全的主要因素进行分类,提取公共因子。并运用人工神经网络（ANN）方法,对这些公共因子进行神经网络训练和预测分析,最终得到遥墙机场停机坪的安全情况。     

飞行员疲劳监测中的人眼定位及状态判别研究

飞行员驾驶疲劳是影响飞行安全的重要因素之一。常用基于人眼识别的方法对飞行员进行疲劳监测，其关键是人眼定位及状态判别。利用改进的平均合成滤波器算法定位人眼，根据模糊综合评价思想，判定人眼睁开闭合状态，根据PERCLOS疲劳检测方法对飞行员进行疲劳监测。实验表明，改进的人眼定位方法具有更高的精确度和鲁棒性，提出的人眼状态判定方法能够满足疲劳监测需要。为最终构建一种机载的、实时的、非接触式的飞行员疲劳监测系统提供了理论基础。     

基于一致性融合和神经网络的航空发动机气路系统故障诊断方法

以信息融合为基础,运用GRNN神经网络对航空发动机气路系统进行故障诊断,提出了一种基于一致性融合和神经网络相结合的故障诊断方法。试验结果表明,该方法能快速识别航空发动机气路系统故障,并且对其他机械设备的故障诊断具有一定的参考价值。