Planning for space station long-duration orbital mission under multi-constraints

An integrated nonlinear planning (NLP) model is built for space station long-duration orbital missions considering both the vehicle visiting schedules and the interaction effects between target phasing, vehicle return adjusting and Earth observation aiming. A two-level optimization approach is proposed to solve this complicated problem. The up-level problem employs the launch times of visiting vehicles as design variables, considers the constraints of crew rotations, resource resupplies and rendezvous launch windows, and is solved by a genetic algorithm. The low-level problems employ the maneuver impulses and burn times within each orbital mission as design variables, and a high-efficient shooting iteration method is proposed based on an analytical equation for the phase angle correction considering the J (2) perturbation. The results indicate that the integrated NLP model for space station long-duration orbital missions is effective, and the proposed optimization approach can obtain the optimal solutions that satisfy the multiple constraints and reduce the total propellant consumption.