To maximize the payload mass, an aerocapture trajectory should be flown in such a way that both the final ΔV and the total heat load are minimized. At very high velocities, the heating due to radiation of high temperature gases in the shock-layer exceeds the heat due to convection. For some aerocapture missions, such a heat load can be 15 times larger than the heat load due to convection. Thus, convective heat load may in some cases be neglected. It is analytically proven that radiative heat load is minimized by the same trajectory that Minimizes The Final Δ V: a bang-bang trajectory, with full lift-up, full lift-down commands. Next, a novel guidance that plans a bang-bang trajectory with constraints in the attitude kinematics is introduced. This allows for achieving an optimal trajectory with only one parameter to be tuned. For the case studied, values of ΔV as low as 100 m/s can be ensured for entry angles between -6° and -5° and a large spectrum of perturbations; with the same guidance, radiative heat load is reduced by up to 20% with respect to traditional aerocapture-guidance methods. Finally, a lateral guidance that makes use of information on the final inclination of the predicted trajectory is introduced. Such guidance allows for very high accuracy in the inclination requirements with only two reversals, and also requires only a single parameter to be tuned. Depending on the tuning, a maximum inclination error of less than 0.1° can be guaranteed.@en

This paper proposes a two-stage optimization approach for aerocapture guidance. In classical entry guidance systems, deterministic optimization is used. Large-scale and short-scale density perturbations may strongly affect the performance of the guidance system, and variations in those are usually not accounted for when computing the command. In this work, perturbations that affect the trajectory at future time-steps are taken into consideration when computing the commanded bank angle. The chosen command is optimal based on a set of possible future perturbations, after the observation of which, a correction can be made. Both two-stage stochastic and two-stage robust optimization are proposed as a solution. In a Monte Carlo analysis consisting of 50 runs, the two-stage robust optimization guidance outperforms an optimal, deterministic, numeric predictor-corrector guidance. Excluding one outlier, also the two-stage stochastic optimization makes the guidance perform better than an optimal deterministic numeric predictor-corrector. With either approach, the computational demands are increased by about thirty times compared to an optimal numeric predictor-corrector. Much of the computation time increase may be reduced by parallelization. On the other hand, the extensive tuning required for the optimal numeric predictor-corrector is not needed for the two-stage optimization guidance, making this approach conceptually more robust. Better modeling of the environment may help further improve the performance. Finally, an approximation to the two-stage robust optimization approach is developed. The guidance has computational requirements only four times larger than those of the optimal numeric predictor-corrector guidance, but can be parallelized into two threads, and, except for two outliers, it offers improved performance.@en