Abstract: Aiming at the need of low-cost and special formation requirements for space missions, the research focuses on the sailcraft utilizing low orbit aerodynamic drag. The practicability of atmospheric sail technology is ascertained via the model based on towing the imitation kite sailcraft to simulate the low orbit environment. And the aerodynamic torque, generated by the rotation of the distributed sub-sails, is applied to complete the position and attitude control of the sailcraft. The dataset is obtained by aerodynamic simulation of sailcraft with different rotation angles of sub-sails and attitudes of sailcraft, which is subsequently used to train an intelligent model based on Genetic Algorithm-optimized Back Propagation Neural Network (GA-BP). The aerodynamic function network model is derived from the dataset, where the predicted correlation coefficient R2 of each parameter is greater than 0.98 (except for the rolling moment of 0.91). The resulting network model is employed in the mechanical equilibrium equations of the sailcraft, leading to the inverse calculation of the control matrix corresponding to the sub-sail rotation angles and the sailcraft relative positions. The control matrix and the boundaries of the sub-sail angles regulate the safe operation range of the sailcraft, which can provide a reference for the actual control of the atmospheric sailcraft.

Key words: distributed atmospheric sail, aerodynamic drag, control matrix, genetic algorithms, neural networks