  Horizon length and model accuracy are defining factors when designing a Model
Predictive Controller. While long horizons and detailed models have a positive
effect on control performance, computational complexity increases. As
predictions become less precise over the horizon length, it is worth
investigating a combination of different models and varying time step size.
Here, we propose a Model Predictive Control scheme that splits the prediction
horizon into two segments. A detailed model is used for the short-term
prediction horizon and a simplified model with an increased sampling time is
employed for the long-term horizon. This approach combines the advantage of a
long prediction horizon with a reduction of computational effort due to a
simplified model and less decision variables. The presented Model Predictive
Control is recursively feasible. A simulation study demonstrates the
effectiveness of the proposed method: employing a long prediction horizon with
advantages regarding computational complexity.
