To improve the speed and accuracy of the reentry trajectory optimization problem of the hypersonic glide vehicle

a reentry trajectory method combining improved sparrow intelligent optimization with parametric design is proposed. Firstly

the population is initialized by the Tent chaotic mapping and the elite reverse population method

and the position of the population is updated by the golden sine strategy. The number of scouts is reduced by the sine strategy

and the optimal solution for the population is selected and updated by the greedy strategy. The global search ability of the algorithm is enhanced without affecting the convergence rate. Then

the hypersonic reentry trajectory optimization problem is transformed into the parametric design problem of the attack angle profile and the bank angle profile

and the path constraint is transformed into the drag acceleration reentry flight corridor to ensure that the path constraint is always satisfied in the reentry process

and the penalty function method is used to ensure that the aircraft can accurately hit the target. Finally

the improved sparrow intelligent optimization algorithm is used to optimize the design parameters to make the objective function optimal. The simulation results show that the proposed improved sparrow algorithm has a faster convergence speed than the original sparrow algorithm

whale algorithm

and particle swarm algorithm

and the accuracy of the hypersonic glide vehicle reentry trajectory is further improved. The Monte Carlo simulation results show that the reentry trajectory optimization algorithm of the hypersonic glide vehicle proposed in this paper has certain robustness.