  Recently, a regional MPC approach has been proposed that exploits the
piecewise affine structure of the optimal solution (without computing the
entire explicit solution before). Here, regional refers to the idea of using
the affine feedback law that is optimal in a vicinity of the current state of
operation, and therefore provides the optimal input signal without requiring to
solve a QP. In the present paper, we apply the idea of regional MPC to min-max
MPC problems. We show that the new robust approach can significantly reduce the
number of QPs to be solved within min-max MPC resulting in a reduced overall
computational effort. Moreover, we compare the performance of the new approach
to an existing robust regional MPC approach using a numerical example with
varying horizon. Finally, we provide a rule for choosing a suitable robust
regional MPC approach based on the horizon.
