  Long prediction horizons in Model Predictive Control (MPC) often prove to be
efficient, however, this comes with increased computational cost. Recently, a
Robust Model Predictive Control (RMPC) method has been proposed which exploits
models of different granularity. The prediction over the control horizon is
split into short-term predictions with a detailed model using MPC and long-term
predictions with a coarse model using RMPC. In many applications robustness is
required for the short-term future, but in the long-term future, subject to
major uncertainty and potential modeling difficulties, robust planning can lead
to highly conservative solutions. We therefore propose combining RMPC on a
detailed model for short-term predictions and Stochastic MPC (SMPC), with
chance constraints, on a simplified model for long-term predictions. This
yields decreased computational effort due to a simple model for long-term
predictions, and less conservative solutions, as robustness is only required
for short-term predictions. The effectiveness of the method is shown in a
mobile robot collision avoidance simulation.
