Sequential Convex Programming For Non-Linear Stochastic Optimal Control

This work introduces a sequential convex programming framework to solve general non-linear, finite-dimensional stochastic optimal control problems, where uncertainties are modeled by a multidimensional Wiener process. We provide sufficient conditions for the convergence of the method. Moreover, we prove that when convergence is achieved, sequential convex programming finds a candidate locally-optimal solution for the original problem in the sense of the stochastic Pontryagin Maximum Principle. We then leverage these properties to design a practical numerical method for solving non-linear stochastic optimal control problems based on a deterministic transcription of stochastic sequential convex programming.Comment: Free-final-time problems with stochastic controls are now discussed in a separate sectio