An intelligent approach for flight risk prediction under icing conditions

Abstract

Flight risk prediction is significant in improving the flight crew's situational awareness because it allows them to adopt appropriate operation strategies to prevent risk expansion caused by abnormal conditions, especially aircraft icing conditions. The flight risk space representing the nonlinear mapping relations between risk degree and the three-dimensional commanded vector (commanded airspeed, commanded bank angle, and commanded vertical velocity) is developed to provide the crew with practical risk information. However, the construction of flight risk space by means of computational flight dynamics suffers from certain defects, including slow computing speed. Accordingly, an intelligent approach for flight risk prediction is proposed to address these defects based on neural networks. Radial Basis Function Neural Network (RBFNN) is optimized using Adaptive Particle Swarm Optimization (APSO). To optimize both the parameters and the structure of APSO-RBFNN, a fitness function containing the training accuracy and network structure size is proposed. Extensive experimental results demonstrate that the flight risk predicted by APSO-RBFNN is very close to that obtained via computational flight dynamics. The average error (RMSE) is less than 10-1. The approach achieves a speedup close to 1000x compared with computational flight dynamics. In addition, some flight upset and recovery cases are presented to illustrate the efficiency of the intelligent approach for flight risk prediction.