  A method is presented to solve a stochastic, nonlinear optimal control
problem representative of spacecraft trajectory design under uncertainty. The
problem is reformulated as a chance constrained nonlinear program, or what is
known as a distribution steering problem. Typical distribution steering
problems rely on the underlying uncertainties to be Gaussian distributions.
This work expands on previous developments by embedding Gaussian mixture
distributions into the formulation to better handle the uncertainty propagation
and chance constraints involved. The method is applied to a finite-thrust
Earth-to-Mars transfer problem. Evaluation via Monte Carlo analysis shows a
greater satisfaction of constraints under non-Gaussian distributions of the
state and a statistically lower cost.
