Convex Relaxations for Nonlinear Stochastic Optimal Control Problems

Abstract

This article presents a new method for computing guaranteed convex and concave relaxations of nonlinear stochastic optimal control problems with final-time expectedvalue cost functions. This method is motivated by similar methods for deterministic optimal control problems, which have been successfully applied within spatial branch-and-bound (B&B) techniques to obtain guaranteed global optima. Relative to those methods, a key challenge here is that the expectedvalue cost function cannot be expressed analytically in closed form. Nonetheless, the presented relaxations provide rigorous lower and upper bounds on the optimal objective value with no sample-based approximation error. In principle, this enables the use of spatial B&B global optimization techniques, but we leave the details of such an algorithm for future work.