  Differential Dynamic Programming is an optimal control technique often used
for trajectory generation. Many variations of this algorithm have been
developed in the literature, including algorithms for stochastic dynamics or
state and input constraints. In this contribution, we develop a robust version
of Differential Dynamic Programming that uses generalized plants and multiplier
relaxations for uncertainties. To this end, we study a version of the Bellman
principle and use convex relaxations to account for uncertainties in the
dynamic program. The resulting algorithm can be seen as a robust trajectory
generation tool for nonlinear systems.
