管制-飞行状态相依网络演化过程

doi:10.7527/S1000-6893.2021.24726

Abstract

Abstract: Prediction of dynamic and accurate operation situation of the control system is essential for collaborative decision-making among relevant units of the air transport system. In this paper, a model for interdependent network of aircraft control/flight status is established based on the conflict of aircrafts, control of aircrafts by controllers, and transfer of controllers. The evolution law of the interdependent network is explored. Correlation analysis and principal component analysis are used to prove the rationality of the five indicators selected. Two simulation scenes of free flight and flight on route are set up. It is proved that each time series is chaotic by calculating the maximum Lyapunov exponent of topological indexes such as average node degree and average point weight. The Long Short-Term Memory (LSTM) method is employed to predict the evolution law of each time series, and is compared with other prediction algorithms. The simulation results show that the LSTM algorithm can predict the evolution process of the control system effectively, and the prediction accuracy is higher than that of the Bayes algorithm. The LSTM algorithm also supports the vector machine algorithm. The prediction error of the five indexes with the LSTM algorithm is mostly within 20% under the condition of free flight, and the prediction effect for flight on route is better than that for free flight.

Key words: interdependent network, prediction, evolution process, Lyapunov exponent, Long Short-Term Memory (LSTM), artificial neural network

CLC Number:

V355

LI Ang, NIE Dangmin, WEN Xiangxi, HAN Baohua, ZENG Yujing. Evolution process of control-aircraft state interdependent network[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(9): 324726-324726.

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Evolution process of control-aircraft state interdependent network

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