STOCHASTIC SEQUENTIAL CONVEX PROGRAMMING FOR ROBUST LOW-THRUST TRAJECTORY DESIGN UNDER UNCERTAINTY

Any space trajectories are subject to state uncertainty due to imperfect state knowledge, random disturbances, and partially known dynamical environments. Ideally, such uncertainty and associated risks must be properly quantified and taken into account in the mission design process to ensure a sufficiently low risk of causing hazardous events. However, designing robust trajectories with such low risk guarantees adds an additional dimension, stochasticity, to space trajectory optimization problems, making it one of the most challenging problems in space mission design. To address the challenge, this study develops a framework that designs robust low-thrust trajectories under uncertainty by combining stochastic optimal control, sequential convex programming, and the augmented Lagrangian method, thereby termed Stochastic Sequential Convex Programming. The developed framework incorporates the stochastic effects of orbit determination and control execution errors into the mission design process, enabling mission designers to concurrently design a reference trajectory and flight-path control plan while satisfying mission constraints in the presence of uncertainty.