Stochastic Optimization for Target Point Approach on Quasi-Halo Orbits

With the growing number of lunar missions operating in environments with highly nonlinear dynamics, the optimization of space flight trajectories and operations under uncertainty has become a priority. This is especially valid for station-keeping on unstable libration-point orbits. This work presents an innovative stochastic optimization method for the target point approach. The method includes uncertainty propagation through differential-algebra-based Monte Carlo analyses directly into the optimization process. Applied to the quasi-halo orbit of the LUMIO CubeSat mission, the method is compared to traditional maneuver-based optimizations of the target point approach, demonstrating significantly improved fuel efficiency.