1.Shanghai Aircraft Airworthiness Certification Center of CAAC;2.College of Safety Science and Engineering, Civil Aviation University of China
飞机着陆阶段是各飞行阶段中风险最大的阶段之一，可能会遇到阵风、跑道入侵、硬着陆和跑道超限等不安全事件。较长的着陆距离会增加着不安全事件发生的风险。为了降低发生不安全事件的风险，本文利用长短期记忆(long short term memory,LSTM)神经网络捕获时间序列飞行数据对时间的依赖性，研究了一种多步滚动预测策略来预测飞机着陆距离以进行实时预警，飞机着陆预测模型用于预测着陆距离。结果表明：与单步预测相比，该方法可以更好地捕捉飞行参数的时间变化。通过多组仿真实验验证基于LSTM神经网络模型的多步滚动预测方法的准确性与有效性。
The aircraft landing phase is one of the most risky phases in each flight phase, and may encounter unsafe events such as gusts, runway incursions, hard landings, and runway overruns. A longer landing distance will increase the risk of unsafe events. In order to reduce the risk of unsafe incidents, this paper uses long short term memory (LSTM) neural network to capture the dependence of time series flight data on time, and studies a multi-step rolling prediction strategy to predict the aircraft landing distance for real-time early warning. The aircraft landing prediction model is used to predict the landing distance. The results show that compared with single-step prediction, this method can better capture the time variation of flight parameters. The accuracy and effectiveness of the multi-step rolling prediction method based on LSTM neural network model are verified by multiple sets of simulation experiments.
蔡宁泊,张程,王伟. 基于长短期记忆神经网络模型的多步滚动预测方法[J]. 科学技术与工程, , ():复制