基于试飞数据的高空长航时无人机任务剖面优化方法  

Mission profile optimization method based on flight test data for high-altitude long-endurance UAV

作　　者：孙健[1] 席亮亮 尹文强[1] SUN Jian;XI Liang-liang;YIN Wen-qiang(Aircraft Flight Test Technology Institute, CFTE, Xi'an 710089, China)

机构地区：[1]中国飞行试验研究院飞机所

出　　处：《飞行力学》2019年第3期79-82,共4页Flight Dynamics

摘　　要：针对高空长航时无人机的任务剖面优化问题,综合考虑无人机的飞行特点,提出了阶梯式的巡航策略。建立了基于试飞数据的无人机任务剖面数学模型,将任务剖面优化问题转化为约束条件下求极值的问题,采用了免疫粒子群算法对问题进行求解。最后,将计算结果应用到飞行试验中,并与传统任务剖面试飞结果进行了对比。结果表明,该方法得到的任务剖面航程明显优于传统任务剖面。The issue of mission profile optimization for the high-altitude long-endurance UAV is studied. Taking into account the flight characteristics of high-altitude long-endurance UAV, a stepwise cruise strategy is proposed. The mathematical model of UAV mission profile based on test flight data is established, and the mission profile optimization issue is transformed into that of extremum under constraint conditions. AI-PSO algorithm is adopted to solve the problem. Finally, the calculated results are applied to the flight test and compared with the test results of the traditional mission profile. The results show that this method is obviously superior to the traditional one.

关 键 词：无人机 剖面优化 飞行试验 免疫粒子群算法 

分 类 号：V279[航空宇航科学与技术—飞行器设计]