Models and Algorithms of Automata Theory for the Control of an Aircraft Group

We develop models and algorithms to control the process of landing aircraft under uncertainty of their positions in space caused by unfavorable, unforeseen external influences. As the objective function we consider the execution time of the dispatcher’s commands, and as constraints we introduce requirements for the safety of aircraft. Based on the developed formalism of generalized synchronization of linear automata, we develop an approach that makes it possible to reduce this problem to an integer linear programming problem. We consider an example of the use of the developed models and methods for controlling the aircraft landing process.     

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.     

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。     

求解机场终端区飞机着陆调度问题的遗传算法

空中管制员需为到达的飞机安排跑道并计算着陆时间,以飞机空中延误最小为出发点研究了多跑道的飞机着陆调度问题,约束条件为每架飞机的着陆时间应落在规定的时间窗内及相邻两架飞机应满足最小时间间隔。针对该问题设计了一种遗传算法对问题进行求解,其中染色体由飞机排序链表和跑道链表组成,相应的交叉和变异算子也做了改进设计。仿真实验用数据库OR-Library中的实例验证了该算法的有效性。     

LANDING AN UNMANNED AIR VEHICLE: VISION BASED MOTION ESTIMATION AND NONLINEAR CONTROL

In this paper, we use computer vision as a feedback sensor in a control loop for landing an unmanned air vehicle (UAV) on a landing pad. The vision problem we address here is then a special case of the classic ego-motion estimation problem since all feature points lie on a planar surface (the landing pad). We study together the discrete and differential versions of the ego-motion estimation, in order to obtain both position and velocity of the UAV relative to the landing pad. After briefly reviewing existing algorithm for the discrete case, we present, in a unified geometric framework, a new estimation scheme for solving the differential case. We further show how the obtained algorithms enable the vision sensor to be placed in the feedback loop as a state observer for landing control. These algorithms are linear, numerically robust, and computationally inexpensive hence suitable for real-time implementation. We present a thorough performance evaluation of the motion estimation algorithms under varying levels of image measurement noise, altitudes of the camera above the landing pad, and different camera motions relative to the landing pad. A landing controller is then designed for a full dynamic model of the UAV. Using geometric nonlinear control theory, the dynamics of the UAV are decoupled into an inner system and outer system. The proposed control scheme is then based on the differential flatness of the outer system. For the overall closed-loop system, conditions are provided under which exponential stability can be guaranteed. In the closed-loop system, the controller is tightly coupled with the vision based state estimation and the only auxiliary sensor are accelerometers for measuring acceleration of the UAV. Finally, we show through simulation results that the designed vision-in-the-loop controller generates stable landing maneuvers even for large levels of image measurement noise. Experiments on a real UAV will be presented in future work.     

基于低通非奇异终端滑模引导的舰载机抗侧风着舰控制技术

针对舰载机的抗侧风着舰控制问题,设计一种低通非奇异终端滑模引导算法（LNTSMC）用于提高舰载机的抗侧风引导能力. 首先, 基于 H_∞混合灵敏度控制设计内环姿态鲁棒控制系统, 并推导舰载机着舰飞行时的侧向引导方程;然后,基于齐次性理论设计一种积分非奇异终端滑模面,使其在满足低通滑模控制器设计要求的同时避免控制奇异现象;进一步,设计一种带边界层的幂指数趋近律来抑制滑模控制中的抖振情况,并引入非齐次干扰观测器来改善边界层内部的鲁棒稳定性; 最后, 通过 Lyapunov 定理证明所设计算法的有限时间稳定性, 并给出仿真结果. 仿真结果表明,所设计的引导算法具有良好的抗侧风性能,通过与已有算法进行对比,验证了所提出算法的优越性.     

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.     

Bid evaluation in combinatorial auctions: optimization and learning

In combinatorial auctions bidders can post bids on groups of items. The problem of selecting the winning bids, called Winner Determination Problem, is NP‐hard. In this paper, we consider an interesting variant of this problem, called Bid Evaluation Problem (BEP), where items are services and are subject to precedence constraints and temporal windows. The BEP has many practical applications, such as, for instance, in transportation routes auctions and in take off and landing time slot allocation problems. We have developed a number of optimization algorithms based on Constraint Programming, on Integer Programming and on the combination of the two techniques. We first show that all algorithms proposed outperform the only commercial system for solving BEP instances called Multi AGent Negotiation Testbed, a more general tool for agent negotiation. Then, we evaluate the developed algorithms and use the decision tree machine learning technique for finding a relation between the instance structure and the solving algorithm and providing an automatic algorithm selection procedure. We show that we can achieve an accuracy of 90% in predicting the best algorithm given the instance to be solved with a significant time saving w.r.t. a single solving technique or a costless, but less accurate, prediction technique. Copyright © 2009 John Wiley & Sons, Ltd.     

Infrared target-flare discrimination using a ZISC hardware neural network

The last generation of infrared imaging aircraft seekers and trackers uses pattern recognition algorithms to find and keep a lock on an aircraft in the presence of decoy flares. These algorithms identify targets, based on the features of the various objects in the missile’s field of view. Because modern both aircrafts and missiles fly faster than sound, speed of operation of the target identifier is critical. In this article, we propose a target recognition system that respects this time constraint. It is based on an artificial neural network implemented in hardware, as a set of parallel processors on a commercially available silicon chip called a ZISC, for zero instruction set computer. This chip would be integrated in the infrared missile seeker and tracker. We describe the characteristics of the images that the image processing module of this seeker and tracker extracts from the infrared video frames and show how to construct from these translation and rotation invariant features that can be used as input to the neural network. We determine the individual discriminating power of these features by constructing their histograms, which allows us to eliminate some as not being useful for our purpose. Finally, by testing our system on real data, we show that it has a 90% success rate in aircraft-flare identification, and a processing time that during this time, the aircrafts and missiles will have traveled only a few millimeters. Most of the images on which the neural network makes its mistakes are seen to be hard to recognize even by a human expert.     

基于单步最优方法的推力矢量垂直短距飞机过渡过程控制

推力矢量垂直短距起降飞机(V/STOL)具有巡航速度快和起降灵活的特点,近年来受到了广泛的关注.然而,该型飞机需要经历悬停转平飞和平飞转悬停的过渡过程阶段,期间飞机会面临强耦合、强非线性等控制难题,使得传统控制器难以胜任.针对上述问题,通过充分考虑执行器的执行能力,提出一种基于单步最优方法的过渡过程控制策略.所提方法结合推力矢量V/STOL飞机的特点,在确保飞机姿态可控的前提下使飞机水平加速度最大,同时使三轴承推力矢量喷管转角向目标转角不断靠近,从而以最快的速度完成过渡过程,并进行控制器切换.仿真实验验证了所提方法具有过渡过程时间短、姿态平稳的优点.     

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.     

Heuristic algorithms for assigning and scheduling flight missions in a military aviation unit

We consider an operations planning problem in a military aviation unit that performs a number of flight missions with multiple identical aircrafts. The problem is to assign the flight missions to the aircrafts and to schedule these assigned missions on each aircraft. Sequence-dependent setup times are required between the missions, and multiple aircrafts may be needed for a mission, but the aircrafts assigned to the same mission should start the mission simultaneously. We develop heuristic algorithms for the problem with the objective of minimizing makespan, i.e., the time by which all the missions have been completed. For evaluation of the performance of the algorithms, a series of computational tests was performed on a number of problem instances, and results show that the proposed algorithms give good or near optimal solutions in a reasonable amount of time.     

基于某型雷达的着陆航迹纠偏监控研究

为了减少飞机着陆阶段的飞行事故,基于某型精密进场雷达设计了飞机着陆航迹纠偏监控设备;通过研究某型雷达的回波数据格式和传输要求,在Windows环境下,采用Microsoft Visual Studio 10.0开发工具,用C#语言开发了着陆航迹纠偏监控软件;该软件能实时、自动监控加入着陆航线的多批次飞机的着陆航迹数据,对超出安全着陆要求的飞机按不同着陆等级设置三类告警门限,在超限事件引发严重事故之前,实时提醒地面人员指挥引导飞机切入安全合适的下滑道进行着陆,解决了某型雷达完全依赖人工监视飞机着陆状态的缺陷;同时,软件可对雷达数据包进行实时采集和误差分析,以作为日常飞行训练质量的辅助分析手段和飞行事故的调查依据。     

特征点和不变矩结合的遥感图像飞机目标识别

传统的飞机目标识别算法一般是通过目标分割,然后提取不变特征进行训练来完成目标的识别。但是,对于实际情况比较复杂的遥感图像飞机目标,至今没有一种适合多种机型的分割和识别算法。针对现有识别算法的不足,本研究提出一种基于特征点空间分布、颜色不变矩和Zernike不变矩相结合的遥感图像飞机目标识别算法。方法：首先,对预处理后的遥感图像和模板图像进行小波变换,在低分辨率图像下采用圆投影特征进行粗匹配,确定候选目标;粗匹配结束后,提取高分辨率图像的多尺度Harris-laplace角点,并画出Delaunay三角网,同时提取出颜色不变矩和Zernike不变矩;然后使用欧氏距离作为这三种特征的相似性度量,并和样本图像进行加权匹配;最后选取欧式距离最小的图像作为最终的识别目标。结果：实验表明,本文算法飞机检测精度比现有算法高2.2%,飞机识别精度比现有算法高1.4%-10.4%。该算法能从遥感图像中精确识别出十大飞机目标,并对背景、噪声、视角变化等多种干扰具有良好的鲁棒性。结论：提出了一种基于特征点空间分布、颜色不变矩和Zernike不变矩相结合的飞机识别算法,该算法使用了图像的多种信息,包括特征点和不变矩,有效地克服了使用单一特征无法描述多种信息的不足。实验结果表明,本文采用基于特征点和不变矩的飞机识别算法比其他算法具有更强的抗干扰能力和识别精度。     

A unified method for evaluating real-time computer controllers and its application

A computer-controlled system is a synergistic coupling of the controlled process and the controller computer. We have defined new performance measures for real-time controller computers based on this coupling. We present a systematic study of a typical critical controlled process in the context of new performance measures that express the performance of both controlled processes and controller computers (taken as a unit) on the basis of a single variable: controller response time. Controller response time is a function of current system state, system failure rate, electrical and/or magnetic interference, etc., and is therefore a random variable. Control overhead is expressed as monotonically nondecreasing function of the response time and the system suffers catastrophic failure, or dynamic failure, if the response time for a control task exceeds the corresponding system hard deadline, if any. The controlled-process chosen for study is an aircraft in the final stages of descent, just prior to landing. Control constraints are particularly severe during this period, and great care must be taken in the design of controllers that handle this process. First, the performance measures for the controller are presented. Second, control algorithms for solving the landing problem are discussed, and finally the impact of our performance measures on the problem is analyzed, showing that the performance measures and the associated estimation method have potential use for designing and/or evaluating real-time controllers and controlled process. In common with all other control techniques, the computational complexity involved in obtaining these measures is susceptible to the curse of dimensionality.     

月球软着陆轨道快速优化

月球软着陆轨道是登月飞行器下降到月球表面轨道中很重要一段的轨道,为了实现飞行器自主软着陆,需要进行快速轨道优化设计.文中根据软着陆轨道的特征和优化算法的特点,对软着陆轨道状态方程做合理的简化处理,优化计算量减少,且更适合优化数值解法求解.在此基础上,使用乘子法处理软着陆终端约束条件,然后利用共轭梯度法求解软着陆轨道.在不同初始条件和终端约束条件下,计算机时小于3秒.仿真结果验证该算法具有收敛速度快、对初始控制量不敏感等优点,易于工程实现.     

Intelligent automatic landing system using time delay neural network controller

This paper presents an intelligent automatic landing system that uses a time delay neural network controller and a linearized inverse aircraft model to improve the performance of conventional automatic landing systems. The automatic landing system of an airplane is enabled only under limited conditions. If severe wind disturbances are encountered, the pilot must handle the aircraft due to the limits of the automatic landing system. In this study, a learning-through-time process is used in the controller training. Simulation results show that the neural network controller can act as an experienced pilot and guide the aircraft to a safe landing in severe wind disturbance environments without using the gain scheduling technique.     

Ellipse proposal and convolutional neural network discriminant for autonomous landing marker detection

Autonomous landing in complex environments is a critical problem for unmanned aerial vehicle autonomous control, and efficiently detecting landing identification mark in real‐world scenario is still challenging. Due to the limited computational power of airborne computing equipment, current target detection algorithms cannot meet the demand efficiently. In this paper, we proposed a new landing marker detection algorithm for autonomous landing systems in a real environment. We used an ellipse detection algorithm to detect the ellipse landmark or other elliptical objects. Furthermore, convolution neural networks were utilized to obtain the correct landmarks, which is robust, fast and can achieve a speed of 25 fps with 720p resolution video on an Intel NUC onboard computer. Unlike the other methods, the accuracy and speed of our algorithm is verified in a real‐time application with more harsh conditions. During system testing, the flight system can detect the object above 20 m, track it, and automatically land on it with this vision algorithm. The algorithm has helped us achieve the first position at Mohamed Bin Zayed International Robotics Challenge in Abu Dhabi this year.     

飞机起落架半主动控制仿真研究

该文总结了飞机起落架的三种控制方法：被动控制,主动控制,半主动控制。传统的起落架控制方法不能使飞机很快的达到稳定,为了解决这一问题,该文分析了半主动控制的优点。建立起了半主动控制起落架的数学模型和线性状态控制方程,并根据现代控制理论分析被动控制起落架系统的稳定性和可控性。采用常规状态反馈的控制方法对起落架系统进行设计,得到了半主动控制器。最后通过仿真软件分别对被动控制和半主动控制的起落架模型进行了仿真分析。仿真结果表明：被动控制起落架有较强的振荡,系统稳定时间也较长。而半主动控制起落架能够有效的降低飞机冲击载荷和振动响应,使飞机很快达到稳定。