Zaragoza Prous, GuillermoGrustan-Gutierrez, EnricFelicetti, Leonard2025-02-192025-02-192025-02-01Zaragoza Prous G, Grustan-Gutierrez E, Felicetti L. (2025) A decreasing horizon model predictive control for landing reusable launch vehicles. Aerospace, Volume 12, Issue 2, February 2025, Article number 1112226-4310https://doi.org/10.3390/aerospace12020111https://dspace.lib.cranfield.ac.uk/handle/1826/23494A novel approach to model predictive control (MPC) with a decreasing horizon is analysed for guiding and controlling reusable launch vehicles (RLVs) during powered descent phases. Conventional MPC methods typically use receding horizons, where optimal control inputs are computed over fixed time intervals. However, when applied directly, these methods can cause a hovering-like behaviour, preventing the vehicle from reaching the landing platform, as the landing time is continually deferred at each iteration. The proposed solution addresses this problem by adjusting the prediction horizon dynamically, reducing its length over time. This dynamic adjustment is driven by a time-scaling factor and the time elapsed since the previous MPC iteration. Optimal control solutions are derived through convex optimization techniques. To evaluate the algorithm’s robustness against initial conditions, a Monte Carlo analysis is performed by varying initial position, velocity and mass. This method can also be used as a viable methodology for selecting tuning parameters for the MPC to ensure a successful and safe landing for a wide range of initial conditions.enAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/4007 Control Engineering, Mechatronics and Robotics40 Engineering4010 Engineering Practice and Education4001 Aerospace engineeringArticle2226-4310563527111122