Reentry trajectory optimization design for lunar return through coevolutionary algorithm

Reentry trajectory optimization with multiple constraints on g-load, dynamic pressure, heat flux on stagnation point of craft and parachute deployment position was studied for low-lift-to-drag lunar return vehicle, and a novel coevolutionary algorithm was presented to solve the parameters optimization problem based on the piece-wise linear bank modulation strategy. Firstly, a piece-wise linear bank modulation versus energy policy was introduced to convert the continuous optimal problem into a finite-dimensional parameter optimization problem. Then, the coevolutionary algorithm consists of escapable particle swarm optimization algorithm and adaptive differential evolution algorithm was employed to solve it. Numerical simulation demonstrates the feasibility of the adopted control parameterization strategy. A performance comparative case was carried out. The coevolutionary algorithm proves to be effective with great accuracy and is well suited for reentry trajectory optimal profile design.