Robust virtual target guidance for the fixed-trim vehicle under multi-constraints

This paper investigates a robust virtual target guidance algorithm for the fixed-trim Maneuverable Reentry Vehicle. The vehicle is faced with multi-constraints including terminal and process constraints. A virtual target integrated spinning guidance law is developed, and the virtual target's capability of handling multi-constraints is validated through analysis. A parametric optimization approach is proposed to determine the virtual target motion. The approach is further developed into a robustness optimization approach, which introduces a measurement of robustness into the optimization and estimates the measurement by Monte Carlo sampling. Illustrative results indicate the constraint-handling capability of the virtual target and the robustness of the proposed guidance algorithm.