Conventional formation flying Earth-orbiting satellites control their orbits to perform their missions using thrusters, but the amount of propellant loaded into a satellite is limited. Therefore, the use of aerodynamic forces for orbit control has been attracting attention, particularly in low Earth orbit. The orbit control can be achieved by appropriately changing the state of satellite, such as attitude and shape, to meet the aerodynamic requirements. In modeling the relationship between satellite state and aerodynamic forces, conventional methods ignore the shielding caused by the nonconvexity of the satellite’s appearance. Ignoring the shielding creates a gap between the modeled and the real aerodynamic forces, resulting in poor control performance. To solve this problem, we propose an aerodynamic force modeling method that incorporates a neural network to estimate the shielding. We train the neural network using data from an aerodynamics simulator. The optimal state that not only generates the required aerodynamic forces but also improves controllability under various mechanical constraints is obtained by solving an optimization problem that incorporates the proposed aerodynamic model. We conduct numerical simulations for establishing and maintaining general circular orbit formations. The results show convergence and continuous stable control of the deputy satellite to the ideal orbit.

传统编队飞行的地球轨道卫星使用推进器控制其轨道以执行任务，但卫星中装载的推进剂数量有限。因此，利用空气动力进行轨道控制一直引起人们的关注，特别是在近地轨道上。轨道控制可以通过适当改变卫星的姿态、形状等状态来满足气动要求。在对卫星状态与气动力之间的关系进行建模时，传统方法忽略了卫星外观的非凸性造成的屏蔽。忽略屏蔽会在模型空气动力与真实空气动力之间产生差距，从而导致控制性能不佳。为了解决这个问题，我们提出了一种气动力建模方法，该方法结合了神经网络来估计屏蔽。我们使用空气动力学模拟器的数据来训练神经网络。通过解决结合所提出的空气动力学模型的优化问题，获得了不仅产生所需的气动力而且提高了各种机械约束下的可控性的最佳状态。我们进行数值模拟来建立和维持一般的圆形轨道结构。结果表明副卫星收敛并持续稳定控制到理想轨道。