The Transition Orbit Optimization Research Of Reentry Vehicle

This paper mainly studies the optimal transition trajectory of a reentry vehicle. According to the different types of tasks, the demands and constraints of the return trajectory are different. This paper studies the situation that the reentry vehicle returns to a given point in a given period of time with terminal velocity constraints. The return trajectory of a reentry vehicle can be divided into transition segment and reentry segment. In this paper, it is assumed that a low thrust engine which can provide dozens of Newtons thrust is used during the orbital maneuver of the vehicle in the transition segment, and in the reentry segment, the vehicle uses ballistic reentry mode. Genetic algorithm is applied with classical optimization method to solve the problem of low thrust orbit optimization and optimal transition orbit of the reentry vehicle is studied.In this paper, the dynamic model of the reentry vehicle in the polar inertial coordinate system is established. After that, the dynamic equations are normalized. Based on the optimal control theory, the sufficient and necessary conditions of the optimum orbit are derived and the initial and terminal conditions are analyzed. By doing so, the issue to be resolved is transformed into Two Point Boundary Value Problem (TPBVP).Float-encoding genetic algorithm is adopted with combination of local search algorithm to solve TPBVP. Genetic operators such as selection, crossover and mutation are designed. Reasonable control parameters in the algorithm are selected. The feasibility of the algorithm is showed through an example.Finally, the genetic algorithm is applied to optimize the transition orbit. Because of the velocity and angle of the reentry point determines the terminal states of the transition segment and can affect the energy consumption and the magnitude of the terminal velocity, they are also treat as optimization parameters. The minimum fuel consumption transition orbit under fixed end-time and terminal velocity constraints is found by using genetic algorithm.Simulation results show that using genetic algorithm to solve trajectory optimization problems can overcome the issue of initial value sensitivity by traditional optimization methods and the global optimum orbit can be found by genetic algorithm.