Adaptive surrogate model-based fast path planning for spacecraft formation reconfiguration on libration point orbits

Abstract

This work considers the optimization of fast path planning for spacecraft formation reconfiguration on libration point orbits. An adaptive surrogate model is proposed for solving the above optimization problem in abnormal conditions and/or emergency cases. First, a multi-layer optimization problem is established. In the outer loop of the multi-layer optimization, the nonlinear optimal control problem, taken as a dynamic optimization, is solved to obtain the optimal transfer paths of all spacecraft in the formation. In the inner loop of the multi-layer optimization, the parameter optimization problem, taken as a static optimization, is solved to obtain the states of all the spacecraft within the maximized area of the spacecraft formation. Then, an adaptive surrogate model is proposed. Because the multi-layer optimization problem is typically time-consuming and computationally expensive, the adaptive surrogate model is constructed using data drawn from the multi-layer optimization model and provides a fast approximation of the objective at new design points. Therefore, the adaptive surrogate model is feasible and cheap for spacecraft formation reconfiguration. Finally, numerical simulations show that the adaptive surrogate-based parameter optimization has advantages over the direct global optimization method. The accuracy, efficiency and robustness of the proposed adaptive surrogate model have also been compared between the different types of surrogate models.