Research On Trajectory Optimization Of Hypersonic Vehicle

Hypersonic vehicles can fly at a high speed and a board airspace, which can rapidly reach any where in the world and be not easy to be blocked and be reusable. There is important value in military and civil within hypersonic vehicle In reality, hypersonic vehicles will be limited by the flight dynamic pressure, the max load factor, aerodynamic heating, and even no-fly zones. Trajectory optimization can make sure the performance of hypersonic vehicles in use, considering various of constraint conditions. So, it has an important practical significance to study the effects on trajectory optimization by different methods and different nodes.The research progress in hypersonic vehicle on the world is introduced in this paper, and the basic principle and the corresponding advantages and disadvantages of common trajectory optimization methods are summarized systematically. Four kinds of methods in the PSOPT software package, that is trapezoid collocation method, Simpson collocation method, Legendre pseudo method and the Chebyshev pseudo method, are used to optimize the GHAME hypersonic climbing trajectory for the goal of minimum fuel consumption, the GHAME hypersonic decline trajectory for the goal of the farthest distance, the CAV hypersonic reentry trajectory for the goal of the farthest distance in two cases that one is without path constraint and the other is with dynamic pressure, load factor, and aerodynamic thermal constrain. The effects of different node number on the GHAME hypersonic climbing trajectory optimization results is studied with the Chebyshev pseudo method.The results show that, in the climbing process of GHAME, the objective function of all methods are similar,but the climbing process are different. In the decline process of GHAME, the results exist big difference. In the reentry process of CAV, the results are very similar. Considering the constraints of the dynamic pressure, the load factor and the aerodynamic thermal, the distance is decreased compared with the unconstrained conditions. For the GHAME climbing trajectory optimization, the effect of the nodes number has no strong regularity, and it is difficult to improve the results of GHAME climb trajectory optimization by simply adding the nodes number.