  Model Predictive Control (MPC) has shown to be a successful method for many
applications that require control. Especially in the presence of prediction
uncertainty, various types of MPC offer robust or efficient control system
behavior. For modeling, uncertainty is most often approximated in such a way
that established MPC approaches are applicable for specific uncertainty types.
However, for a number of applications, especially automated vehicles,
uncertainty in predicting the future behavior of other agents is more suitably
modeled by a twofold description: a high-level task uncertainty and a low-level
execution uncertainty of individual tasks. In this work, we present an MPC
framework that is capable of dealing with this twofold uncertainty. A scenario
MPC approach considers the possibility of other agents performing one of
multiple tasks, with an arbitrary probability distribution, while an analytic
stochastic MPC method handles execution uncertainty within a specific task,
based on a Gaussian distribution. Combining both approaches allows to
efficiently handle the twofold uncertainty structure of many applications.
Application of the proposed MPC method is demonstrated in an automated vehicle
simulation study.
