Spacecraft Reentry Trajectory Optimization Based On Swarm Intelligence Algorithm

Deep space exploration is one of the hot spots in human research.After beinglaunched from the ground into space and completing the mission,the spacecraft enters the atmosphere from outer space and returns to the earth's surface safely and accurately.Trajectory optimization is the most important part of the preliminary design phase of the spacecraft.The near-earth spacecraft enters the atmosphere at a speed of 7.8k m/s at a distance of 110 kilometers from the ground.The trajectory optimization determines whether the spacecraft can return to the ground safely.This paper focuses on the spacecraft orbital reentry as the background and studies spacecraft trajectory optimization problems.The main aspects are listed as follows:Firstly,the spacecraft kinematics model and the constraints of the spacecraft in the reentry process have been given.Based on the plane motion equation of spacecraft,the effects of different lift-drag ratio,reentry angle and reentry velocity on the reentry trajectory of spacecraft also have been analyzed.Based on the decimal ant colony algorithm,the spacecraft jump-in and reentry trajectory has been optimized.Aiming at the problem that the traditional trajectory optimization method is sensitive to the initial value,the decimal ant colony algorithm has been applied to the trajectory optimization.The mathematical model of spacecraft reentry trajectory optimization under overload constraints has been given.the model has been normalized and discretized,and the continuous trajectory optimization problem turned into a parameter optimization problem.The decimal ant colony algorithm has been used to obtain the optimal lift coefficient solution.Simulation results verify the effectiveness of the method.The particle swarm algorithm and ant colony algorithm has been applied to optimize the spacecraft jump-in and reentry trajectory.For the decimal ant colony algorithm,it is easy to trap in the local extremum and has a large dependence on parameter Rh₀,Q and K,which is difficult to obtain.The decimal ant colony algorithm has been improved.Combining particle swarm optimization algorithm with ant colony algorithm,the parameters have been determined through continuous search in continuous space.The simulation results show that the particle swarm algorithm can quickly obtain the value of the parameterRh₀,Q and K effectively jump out of the local optimum and obtain the optimal lift coefficient solution as a result.