Onboard Trajectory Generation of Hypersonic Morphing Aircraft

In this paper, a trajectory optimization strategy for the hypersonic morphing aircraft is proposed, and the AMPI method is used to generate the online trajectory with initial state errors. Firstly, the aerodynamic model and propulsion model of the hypersonic morphing aircraft were established considering the wingspan and the scramjet. Secondly, the optimization strategy was proposed via Gauss pseudospectral method considering the control variables including angle of attack (AOA) and wingspan. The optimized trajectory met the final constraints and path constraints with the objective to minimize the time of the ascent phase. Then, the AMPI method was used to generate online trajectory without solving OCP or NLP on the base of trajectory database calculated by the optimization strategy. The simulation results indicate high accuracy of AMPI method and the final errors corresponding to different initial errors were acceptable. The mean value of the CPU time of the method was about 0.1 second, which shows real-time capability.

Novel method for hurricane trajectory prediction based on data mining

This paper describes a novel method for hurricane trajectory prediction based on data mining (HTPDM) according to the hurricane's motion characteristics. Firstly, all frequent trajectories in the historical hurricane trajectory database are mined by using association analysis technology and their corresponding association rules are generated as motion patterns. Then, the current hurricane trajectories are matched with the motion patterns for predicting. If no association rule is found for matching, a predicted result according to the hurricane current movement trend would be returned. All experiments are conducted with the Atlantic weather Hurricane/Tropical Data from 1900 to 2008. The experimental results show that if the matching failure part is contained, the prediction accuracy is 57.5%. Whereas, the valve would be to 65% provided all matches are successful.

Novel method for hurricane trajectory prediction based on data mining

This paper describes a novel method for hurricane trajectory prediction based on data mining (HTPDM) according to the hurricane's motion characteristics. Firstly, all frequent trajectories in the historical hurricane trajectory database are mined by using association analysis technology and their corresponding association rules are generated as motion patterns. Then, the current hurricane trajectories are matched with the motion patterns for predicting. If no association rule is found for matching, a predicted result according to the hurricane current movement trend would be returned. All experiments are conducted with the Atlantic weather Hurricane/Tropical Data from 1900 to 2008. The experimental results show that if the matching failure part is contained, the prediction accuracy is 57.5%. Whereas, the valve would be to 65% provided all matches are successful.