@article{GAO2026, 
author = {Duozhi GAO and Yanning GUO and Edoardo FADDA and Youmin GONG and Chuanjiang LI and Paolo BRANDIMARTE},
title = {Robust powered descent guidance considering mass and fuel consumption uncertainties: A convex optimization approach},
year = {2026},
journal = {Chinese Journal of Aeronautics},
volume = {39},
number = {3},
keywords = {Uncertainty, Trajectory optimization, Convex optimization, Powered descent, Closed-loop guidance},
url = {https://www.sciopen.com/article/10.1016/j.cja.2025.103914},
doi = {10.1016/j.cja.2025.103914},
abstract = {This paper addresses the challenge of mass uncertainty during the powered descent phase of a Mars lander and proposes a robust powered descent guidance algorithm that accounts for uncertainties in mass and fuel consumption. First, the traditional trajectory optimization method based on convex optimization is improved by developing a fast and accurate solution approach using sequential convex optimization. Second, the effects of mass uncertainty on position are modeled and analyzed, with corresponding computational methods provided for different scenarios. Third, the worst-case scenario under mass uncertainty is analyzed through both geometric and theoretical approaches, and a modified glide-slope constraint method is proposed to ensure safe landing even in adverse conditions. Moreover, a closed-loop receding horizon based guidance is developed to further mitigate the effects of mass uncertainty and improve terminal landing accuracy. Finally, the proposed improved convex optimization algorithm and robust trajectory optimization algorithm are validated through simulation cases and compared with a probabilistic approach. The simulations further test various initial positions, velocities, and glide-slope angles, demonstrating that the solutions are both accurate and robust.}
}