大型飞机纵向轨迹优化设计及算法研究

发布时间：2018-11-11 18:36

【摘要】：在保护国家安全方面,飞机担负着很大的责任,在战争中可以占据很大的优势,而有效的飞行轨迹优化对战术飞行方面至关重要。因此,本文研究了基于改进粒子群算法的大型飞机纵向飞行剖面的设计及其算法,以达到纵向飞行轨迹优化的目的。文中首先系统阐述了飞机纵向轨迹优化的模型及原理。建立了飞机数学模型,分析了轨迹优化的原理,介绍了飞机轨迹优化的性能指标。数学模型包括运动模型和大气环境模型,而运动模型主要指飞机运动学方程。轨迹优化原理方面主要介绍了庞特里亚金最小值原理和飞机的能量状态近似法。然后本文介绍了基本粒子群算法的背景、原理及流程。粒子群算法目前在决策支持、多目标优化、信号处理及模式识别等领域被广泛运用。接着在深入研究基本粒子群算法的基础上改进了粒子群算法,然后在大型飞机纵向轨迹优化过程中运用了该算法并取得了良好的效果。优化过程中首先深入分析了爬升段、巡航段以及下降段的优化原理,随后构建了爬升段、下降段以及巡航段的适应度函数,接着通过惯性权重的设置改进了基本粒子群算法,最后运用该算法分别对巡航段、爬升段和下降段的成本进行了寻优。最后通过Matlab软件进行仿真,并给出结果分析。比较分析得出的结论是：节省燃料的原则是增加爬升和巡航高度,缩短爬升距离,增加巡航距离,降低爬升和巡航速度；而以最小时间为性能指标飞行时,则降低爬升和巡航高度,增加爬升距离,减少巡航距离,提高爬升和巡航速度。由耗油最少和时间最短飞行模式比较可知,二者是相互矛盾的,因此,需要根据实际情况选取合适的操作模式,或者依据实际需要加入时间成本指数,取二者的平衡点,以圆满完成飞行任务。
[Abstract]:In the aspect of protecting national security, aircraft have a great responsibility and can occupy a great advantage in war, and effective flight trajectory optimization is very important to tactical flight. Therefore, this paper studies the design and algorithm of longitudinal flight profile of large aircraft based on improved particle swarm optimization (PSO) algorithm, in order to achieve the purpose of longitudinal flight trajectory optimization. Firstly, the model and principle of aircraft longitudinal trajectory optimization are described systematically. The mathematical model of aircraft is established, the principle of trajectory optimization is analyzed, and the performance index of aircraft trajectory optimization is introduced. The mathematical model includes the motion model and the atmospheric environment model, while the motion model mainly refers to the aircraft kinematics equation. In the aspect of trajectory optimization principle, the principle of Ponteriagin minimum value and the approximate method of aircraft energy state are introduced. Then this paper introduces the background, principle and flow of basic particle swarm optimization. Particle swarm optimization (PSO) is widely used in decision support, multi-objective optimization, signal processing and pattern recognition. Then the particle swarm optimization algorithm is improved on the basis of deep research on the basic particle swarm optimization algorithm, and then the algorithm is applied to the longitudinal trajectory optimization of large aircraft and good results are obtained. In the process of optimization, the optimization principle of climbing section, cruise section and descent section is analyzed deeply, then the fitness function of climbing section, descent section and cruise section is constructed, and then the basic particle swarm optimization algorithm is improved by setting inertia weight. Finally, the algorithm is used to optimize the cost of cruise section, climb section and descent section. Finally, the simulation is carried out by Matlab software, and the result is analyzed. The conclusion of comparative analysis is that the principle of saving fuel is to increase climbing and cruising altitude, shorten climbing distance, increase cruising distance, and reduce climbing and cruising speed; When flying with the minimum time as the performance index, the climbing and cruising altitude will be reduced, the climbing distance will be increased, the cruise distance will be reduced, and the climbing and cruising speed will be improved. The comparison between the minimum fuel consumption and the shortest flight time shows that the two modes are contradictory. Therefore, it is necessary to select the appropriate operation mode according to the actual situation, or to add the time cost index according to the actual need to take the balance point between the two. To complete the mission successfully.
【学位授予单位】：青岛科技大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：V249.1;TP18

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