Reentry blackout reachable set footprint prediction using multi-phase trajectory optimization.

• Reachable set footprint (RSF) after blackout is predicted by trajectory optimization. • Successive convex programming is used to determine RSF with intermediate states. • RSF shrinks with decreasing altitude of additional state observation during blackout. • A decision altitude exists to determine whether to update RSF with new observation. Blackout emerges in the reentry phase of reusable launch vehicles (RLV). Therein, large uncertainties exist in the telemetry signals of RLV, leading to potential safety problems. To facilitate predicting possible ranges of RLV final position when leaving blackout, this paper proposes a modified approach for computing reachable set footprint (RSF). A multi-phase trajectory optimization method is applied to simplified dynamics of RLV. Specifically, partial final boundary conditions are additionally supplemented to the first phase to exploit the intermediate state information during blackout. On this basis, RSF is predicted via solving a series of trajectory optimization problem by sequential convex programming. RSF with additional state information from different altitude are compared in numerical cases. Simulation results show that there exists a suitable range to update RSF using intermediate information. The decision altitude of updating RSF is determined for the exemplary RLV. [ABSTRACT FROM AUTHOR]