Sequential convex programming for non-linear stochastic optimal control

This work introduces a sequential convex programming framework for non-linear, finitedimensional stochastic optimal control, where uncertainties are modeled by multidimensional Wiener process. We prove that any accumulation point of the sequence iterates generated is candidate locally-optimal solution original problem in sense Pontryagin Maximum Principle. Moreover, we provide sufficient conditions existence at least one such point. then leverage these properties to design practical numerical method solving non-linear control problems based on deterministic transcription programming.