  The trade-off between optimality and complexity has been one of the most
important challenges in the field of robust Model Predictive Control (MPC). To
address the challenge, we propose a flexible robust MPC scheme by synergizing
the multi-stage and tube-based MPC approaches. The key idea is to exploit the
non-conservatism of the multi-stage MPC and the simplicity of the tube-based
MPC. The proposed scheme provides two options for the user to determine the
trade-off depending on the application: the choice of the robust horizon and
the classification of the uncertainties. Beyond the robust horizon, the
branching of the scenario-tree employed in multi-stage MPC is avoided with the
help of tubes. The growth of the problem size with respect to the number of
uncertainties is reduced by handling \emph{small} uncertainties via an
invariant tube that can be computed offline. This results in linear growth of
the problem size beyond the robust horizon and no growth of the problem size
concerning small magnitude uncertainties. The proposed approach helps to
achieve a desired trade-off between optimality and complexity compared to
existing robust MPC approaches. We show that the proposed approach is robustly
asymptotically stable. Its advantages are demonstrated for a CSTR example.
