Constellation configuration design is a prerequisite and critical step in the construction of a mega-constellation system in low Earth orbit. However, the huge number of satellites and the intricate changes in relative positions among them make the configuration design the most challenging part. In this paper, we propose a configuration design scheme for mega-constellations considering collision-avoidance constraints with the objective of uniform global coverage. In this design scheme, the constellation is made up of multiple Walker constellations with the same orbital altitude and different orbital inclination. Moreover, the analytical expression for the minimum distance between any two satellites in the same orbital altitude is derived, and the constellation internal collision-avoidance constraint is established accordingly. Finally, a permanent inter-satellite link design scheme without dynamic reconstruction is presented based on the mega-constellation configuration. Simulation results show that the mega-constellation design scheme introduced in this paper can achieve relatively uniform global coverage (its N Asset Coverage ranges from 18 to 25). The mixed Walker constellation is capable of providing a greater number of N Asset Coverage for most of the world than the Walker constellation of the same satellite order of magnitude. In addition, the inter-satellite link scheme designed in this paper can ensure continuous and stable communication between any satellite nodes.

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A switching neural network control scheme, consisting of the adaptive neural network controller and sliding mode controller, is proposed for underactuated formation reconfiguration in elliptic orbits with the loss of either the radial or in-track thrust. By using the inherent coupling of system states, the switching neural network technique is then adopted to estimate the unmatched disturbances and design the underactuated controller to achieve underactuated formation reconfiguration with high precision. The adaptive neural network controller works in the active region, and the disturbances composed of linearization errors and external perturbations are approximated by radial basis function neural networks. The adaptive sliding mode controller works outside the active region, and the upper bound of the approximation errors is estimated by the adaptation law. The stability of the closed-loop control system is proved via the Lyapunov-based approach. The numerical simulation results have demonstrated the rapid, high-precision and robust performance of the proposed controller compared with the linear sliding mode controller.

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