  Deadbeat Robust Model Predictive Control (DRMPC) is introduced as a new
approach of Robust Model Predictive Control (RMPC) for linear systems with
additive disturbances. Its main idea is to completely extinguish the effect of
the disturbances in the predictions within a small number of time steps, called
the deadbeat horizon. To this end, explicit deadbeat input sequences are
calculated for the vertices of the disturbance set. They generalize to a
nonlinear disturbance feedback policy for all disturbances by means of a
barycentric function. Similar to previous approaches, this disturbance feedback
policy can be either part of the online optimization (Online DRMPC) or
pre-calculated during the design phase of the controller (Offline DRMPC). The
main advantage over all other RMPC approaches is that no Robust Positive
Invariant (RPI) set has to be calculated, which is often intractable for
systems with higher dimensions. Nonetheless, for Online DRMPC and Offline DRMPC
recursive feasibility and input-to-state stability can be guaranteed. A small
numerical example compares the two versions of DRMPC and demonstrates that the
performance of DRMPC is competitive with other state-of-the-art RMPC
approaches. Its main advantage is its easy extension to linear time-varying
(LTV) and linear parameter-varying (LPV) systems.
