飞机着陆调度排序算法的设计与实现

航空管制员必须为同时到达的每一架飞机计算着陆时间，使整体费用最小，同时还要注意一些硬性的限制条件。在某一时刻，给定管制员视野内的飞机数量，可以公式化为约束最优化问题，从而应用一定的算法来解决。该文提出了基于分枝定界的飞机着陆调度排序算法——ASAL，实验证明通过该算法能够很好地解决飞机着陆调度优化问题。     

基于uppaal的飞机着陆控制系统模型验证

为了避免飞机在着陆过程中出现事故,同时又能充分利用机场的跑道资源,对飞机数量多于跑道的情况进行了研究,采用了模型验证的方法.介绍了时间自动机的相关理论,以及基于该理论的验证工具uppaal,在此基础上使用uppaal工具对飞机着陆过程构造了模型,然后对模型的需求规范进行了验证,验证结果表明该模型不存在死锁问题,最终可以保证飞机安全和及时地着陆.     

进近阶段飞机着陆调度优化

飞机着陆调度问题属于NP-hard问题,文中建立了进近阶段调度模型,在此模型基础上提出了一种改进的遗传算法来求解此问题.该算法基于双染色体编码方案,构建了满足MPS约束的初始种群,给出了启发式选择算子和自适应变异算子.针对多跑道飞机着陆调度问题,提出了随机分配和选择分配两种跑道分配策略.仿真结果表明,该方法能有效地减少飞机着陆调度中的总延迟,使待着陆飞机快速有序地进入机场着陆.     

复杂环境下基于价格时间自动机飞机着陆调度

目前航空运输系统飞机着陆调度环节普遍存在调度策略单一、效率低、附加成本较高等不足,提出了以价格时间自动机作为基础模型架构的飞机着陆调度设计,在满足着陆时间窗、最小尾流间隔等约束条件下优化额外成本消耗,并考虑复杂气候地理环境,构建出飞机着陆过程中各交互实体的价格时间自动机模型,采用UPPAAL CORA中的分支界定算法求解飞机着陆调度最优成本的可达性.仿真实验结果表明:此研究方法可应用于复杂环境下、大吞吐量的飞机着陆调度,能够显著降低着陆消耗,提升跑道容量,具有安全性、智能性与经济性.     

某飞机着陆的数值计算

为合理设计飞机着陆程序,采用数字仿真和高精度计算技术,通过分析飞行实际情况研究起落架、襟翼和地面效应对飞机空气动力特性的影响,建立某飞机着陆的精确数值模型,并用差分法离散该模型.编制数值计算程序实现飞机的地面滑跑和空中运动仿真,并利用该程序进行飞机着陆的数值分析.该研究有利于智能选择飞机最优着陆程序、保障飞行安全、提高...     

面向场面滑行的机场关键资源蚁群调度模型*

每一架飞机场面滑行时间的长短和飞机总体滑行时间的均衡性反应了机场调度的合理性。跑道和停机位的分配直接决定了飞机的滑行时间。根据不同航班占用停机位的时间不同,将航班的停机位分配约束关系表示成图的权值0-1。充分考虑跑道容量等约束条件,对航班进行跑道初始化分配。基于停机位类型、航班类型、经计算得出的图的权值和跑道分配结果,运用蚁群算法,以最少数量的航班分配到远停机位和飞机总体滑行时间的均衡性为目标函数,对航班进行停机位分配。然后根据停机位分配结果,对跑道分配进行调整,反复迭代求出最优结果,并对枢纽机场进行调度仿真,验证了算法的合理性,可作为机场调度的参考。     

带注意力机制的神经网络用于跑道线检测

为了提高无人机着陆的有效性，设计了一种带有注意力机制的神经网络算法（LineNet）实现无人机跑道线检测。根据无人机着陆场景仿真出样本数据，并利用标注工具对数据进行标注，使用Shuffle Conv模块缓解特征融合计算量的占用问题，并引入空洞空间金字塔池化注意力机制（ASPP-SA）,ASPP-SA模块中添加残差的跳跃结构获取更多的图像信息，对LineNet模型的检测结果做形态学处理并结合连通区域约束对跑道线特征点进行分类，对相同类别的特征点通过最小二乘法进行跑道线拟合。经仿真数据验证，设计的方法可以有效地检测和识别出正确的跑道线，其平均检测精度为94.36%，相较于LaneNet算法、SegNet算法分别提高了12.82和9.95个百分点，单帧检测时间17.2 ms，是CNN+Hough变化算法1.5倍左右，可以满足无人机着陆的响应时间需求，在无人机着陆的研究过程中有重要意义。     

无人机基于视觉自主着陆的跑道识别跟踪

跑道检测识别与跟踪是基于视觉的无人机自主着陆的前提和难点,本文根据固定翼无人机基于视觉自主着陆的特点设计了包括跑道检测、跑道特征提取、跑道识别和跑道跟踪的方案,并在ARM Cortex-A9处理器中基于Linux系统使用OpenCV实现了该方案.最后按照逐步递进的方式分别对检测、检测识别、检测识别与跟踪结果进行了实验验证,并对实时性进行了分析.实验结果表明,通过该方案可以准确地识别图像中的跑道并具有较快的跟踪速度.     

基于干扰估计的非对称运动下飞机刹车系统模型预测控制

针对飞机在非对称运动下的双侧机轮协调控制问题, 提出一种基于滑模干扰估计的模型预测控制方法. 首先, 通过对飞机制动过程横纵方向力矩机理分析并分别考虑左右机轮对刹车性能的影响, 建立全面刻画系统动态的地面滑跑动力学模型. 在此基础上, 设计滑模观测器对侧风干扰进行实时估计, 利用补偿机制实现对侧风扰动的有效抑制. 此外, 提出基于前轮荷载状态门限特征和结合系数阈值范围特征的分析方法, 解决切换跑道环境辨识问题. 设计非线性模型预测算法, 实现飞机纵向防滑刹车和横向跑道纠偏的协调控制. 最后, 在侧风干扰、跑道切换以及不对称着陆等情况下进行仿真实验, 验证了所提出的控制策略能够有效提升刹车系统的防滑效率及纠偏性能.     

空中自动防撞系统最优逃避机动的确定

采用最优控制理论对空中自动防撞系统确定其最优的逃避机动.首先将空中自动防撞系统逃避机动的确定问题抽象为一个具有复合性能指标的可变终端时刻的最优控制问题,然后采用二次曲线拟合的方法,求出两架飞机最靠近的终端时刻.最后根据极大值原理和飞机的状态方程,推算出为使两架飞机在最靠近时,实现最大距离的逃避机动所应采用的最优滚转角速度和法向过载,并给出了最优解的解析表达式.     

Application of generalised neural network for aircraft landing control system

 It is observed that landing performance is the most typical phase of an aircraft performance. During landing operation the stability and controllability are the major considerations. To achieve a safe landing, an aircraft has to be controlled in such a way that its wheels touch the ground comfortably and gently within the paved surface of the runway. The conventional control theory found very successful in solving well defined problems, which are described precisely with definite and clearly mentioned boundaries. In real life systems the boundaries can't be defined clearly and conventional controller does not give satisfactory results. Whenever, an aircraft deviates from its glide path (gliding angle) during landing operation, it will affect the landing field, landing area as well as touch down point on the runway. To control correct gliding angle (glide path) of an aircraft while landing, various traditional controllers like PID controller or state space controller as well as maneuvering of pilots are used, but due to the presence of non-linearities of actuators and pilots these controllers do not give satisfactory results. Since artificial neural network can be used as an intelligent control technique and are able to control the correct gliding angle i.e. correct gliding path of an aircraft while landing through learning which can easily accommodate the aforesaid non-linearities. The existing neural network has various drawbacks such as large training time, large number of neurons and hidden layers required to deal with complex problems. To overcome these drawbacks and develop a non-linear controller for aircraft landing system a generalized neural network has been developed.     

Transient analysis of flexible multi-body systems. Part II: Application to aircraft landing

This investigation presents a method for transient analysis of a large-scale multi-body aircraft consisting of interconnected rigid and flexible bodies that undergo large angular rotations. Elastic components of the aircraft are discretized using the finite element method. The system equations of motion and nonlinear algebraic constraint equations describing joints between different components are written in the Lagrangian formulation using a finite set of coupled reference and modal coordinates. The system differential equations of motion and algebraic constraint equations are computer-generated and integrated forward in time using an explicit-implicit direct numerical integration algorithm coupled with a Newton-Raphson type iteration in order to check on constraint violations. Impact and intermittent motion events are accounted for by using a generalized momentum balance that predict jump discontinuities in the generalized velocities as well as jump discontinuities in the system reaction forces. The formulation presented and the computer program developed are used to simulate the impact between the landing gear and the runway. The method is also used to predict the dynamic behavior of the aircraft during the traverse of an abrupt elevation change in the runway.     

基于LSD检测的无人机视觉着陆定位算法

针对复杂电磁干扰和拒止环境下固定翼无人机自主着陆的应用场景,提出了一种基于LSD的无人机视觉着陆定位算法,通过检测跑道的左右边线以及起始线对无人机进行定位;根据机场跑道的形态学特征,构建机场跑道数据模型,并对实验所用相机进行标定,采用灰度化和高斯滤波对采集到的图像进行预处理,采用LSD直线检测算法以提取跑道的直线特征,设计几何滤波策略从直线特征中提取出跑道的三条边线,采用蒙版技术以提高检测算法的抗干扰能力;根据相机成像原理推导出基于线检测的PNP定位算法,通过检测得到的机场跑道线在像素坐标系下的位置信息求出无人机相对于跑道的三维位置;分别在视景仿真环境和真实机场环境进行检测和定位解算,结果满足无人机着陆定位实时性和准确性的要求,从而验证了视觉着陆定位算法的有效性。     

An efficient hybrid particle swarm optimization algorithm in a rolling horizon framework for the aircraft landing problem

This paper proposes a hybrid particle swarm optimization algorithm in a rolling horizon framework to solve the aircraft landing problem (ALP). ALP is an important optimization problem in air traffic control and is well known as NP-hard. The problem consists of allocating the arriving aircrafts to runways at an airport and assigning a landing time to each aircraft. Each aircraft has an optimum target landing time determined based on its most fuel-efficient airspeed and a deviation from it incurs a penalty which is proportional to the amount of deviation. The landing time of each aircraft is constrained within a specified time window and must satisfy minimum separation time requirement with its preceding aircrafts. The objective is to minimize the total penalty cost due to deviation of landing times of aircrafts from the respective target landing times. The performance of the proposed algorithm is evaluated on a set of benchmark instances involving upto 500 aircrafts and 5 runways. Computational results reveal that the proposed algorithm is effective in solving the problem in short computational time.     

Fuzzy modelling control for aircraft automatic landing system

Aircraft landing control based on fuzzy modelling networks is presented. The proposed scheme uses a fuzzy controller combined with a linearized inverse aircraft model. A multi-layered fuzzy neural network is used as the controller, providing the control signals at each stage of the aircraft-landing phase. The algorithm used to train the network is the Backpropagation Through Time. The linearized inverse aircraft model provides the error signals that will be used to back-propagate through the controller at each stage. The objective of this study is to improve the performance of conventional automatic landing systems. The simulation results are described for the automatic landing system of a commercial aeroplane. Tracking performance and robustness are demonstrated through software simulations. Simulation results show that the fuzzy controller can successfully expand the safety envelope to include more hostile environments such as severe turbulence.     

基于IABC算法的舰载机着舰调度

舰载机有序、高效着舰是确保舰载机舰面保障计划如期进行的必要前提,为提高舰载机着舰效率并减轻传统人工着舰排序的负担,研究一种舰载机着舰调度算法.首先,以加权着舰完成时间和为优化目标,构建舰载机着舰调度的数学模型;其次,提出一种改进的人工蜂群算法用于模型求解,算法在基本人工蜂群算法的基础上引入遗传算法中的交叉算子、精英策略以及一系列自适应局部搜索策略,以增强算法的全局搜索性能,提高算法收敛速度;最后,通过着舰调度案例仿真和算法对比表明,改进的人工蜂群算法具备更强的优化性能和更好的鲁棒性,可以求解大规模舰载机着舰调度问题,具有工程实际应用价值.     

Aircraft landing problems with aircraft classes

This article focuses on the aircraft landing problem that is to assign landing times to aircraft approaching the airport under consideration. Each aircraft’s landing time must be in a time interval encompassing a target landing time. If the actual landing time deviates from the target landing time additional costs occur which depend on the amount of earliness and lateness, respectively. The objective is to minimize overall cost. We consider the set of aircraft being partitioned into aircraft classes such that two aircraft of the same class are equal with respect to wake turbulence. We develop algorithms to solve the corresponding problem. Analyzing the worst case run-time behavior, we show that our algorithms run in polynomial time for fairly general cases of the problem. Moreover, we present integer programming models. We show by means of a computational study how optimality properties can be used to increase efficiency of standard solvers.     

A novel trajectory planning strategy for aircraft emergency landing using Gauss pseudospectral method

To improve the survivability during an emergency situation, an algorithm for aircraft forced landing trajectory planning is proposed. The method integrates damaged aircraft modelling and trajectory planning into an optimal control framework, in order to deal with the complex aircraft flight dynamics, a solving strategy based on Gauss pseudospetral method （GPM） is presented. A 3-DOF nonlinear mass-point model taking into account the wind is developed to approximate the aircraft flight dynamics after loss of thrust. The solution minimizes the forced landing duration, with respect to the constraints that translate the changed dynamics, flight envelope limitation and operational safety requirements. The GPM is used to convert the trajectory planning problem to a nonlinear programming problem （NLP）, which is solved by sequential quadratic programming algorithm. Simulation results show that the proposed algorithm can generate the minimum-time forced landing trajectory in event of engine-out with high efficiency and precision.