Optimization Research On A Period Of Hypersonic Vehicle Skip Flying Trajectory

In the 21st centuries, Hypersonic Vehicle is a main direction in the development of the aerospace industry and plays an increasingly important role in the military, political and economic fields. Therefore, it has more significance to optimize its skip flying trajectory. In a period of skip flying trajectory, the value of many factors such as original mass, muzzle velocity, angle of muzzle velocity, wing area and lift-to-drag ratio can influence its flying trajectory and other design-parameters greatly. Accordingly, these different factors should be taken into account together to get a group of optimal values.Firstly, the paper summarizes the Hypersonic Vehicle and Particle Swarm Optimization Algorithm. Certain elementary knowledge about research status, flight environment, flying trajectory of Hypersonic Vehicle and PSO algorithm, which would be used in the optimization research are briefly introduced.Secondly, three process models and a density model are founded to analyze the six states in the flying process of hypersonic vehicle. Subsequently, the optimization objects and the optimization constraints are also put forward.Then the relationships among original mass, original angle, wing area, lift-to-drag ratio and level distance are studied and tested. According to the test results, a modified PSO algorithm is used to perform the optimization research on a period of skip flying trajectory. By testing and analyzing the test example, the method has been successfully used and validated.Finally, in the optimization process mentioned above, the value of PSO algorithm's basic parameters could be given only by empirical value. According to this, these parameters in this part are given by uniform design method which is used to covert the problem of parameter establishment into the experimental design of multi-factor and multilevel. And then the modified PSO algorithm is also used to perform the optimization research on a period of skip flying trajectory. After analyzing the test results, the uniform design and the optimization research were repeated. The test results of the example have been successfully proved that the method is rational and valid.