一类不确定环境下的再入滑翔飞行器轨迹规划

The flight process of reentry vehicles requires traversing a vast area from the near space to the ground. During this process, even minor modeling errors and external disturbances can lead to deviations from the original target point or exceed the constraint boundaries. To enhance the robustness of the results, this paper investigates a trajectory planning method for reentry vehicles under uncertain environments and introduces the concept of data-driven robust optimization to address uncertainties. A data-driven robust optimization trajectory planning approach is proposed. The core idea of the proposed method is to dynamically construct uncertainty sets using historical data of uncertain parameters and then solve the problem incorporating these sets using robust optimization techniques. Compared to traditional robust optimization or chance-constrained optimization, the proposed method offers two significant advantages: First, it does not require prior knowledge about the distribution or range of uncertain parameters, nor does it demand that they conform to a specific form. Second, by constructing data-driven support vector clusters online, the optimization results are less conservative. To improve computational efficiency, the method is further tailored according to the characteristics of reentry optimization problems. Numerical simulation results are presented and compared with traditional methods to demonstrate the effectiveness of the proposed approach.