  We present a robust model predictive control (MPC) framework for linear
systems facing bounded parametric uncertainty and bounded disturbances. Our
approach deviates from standard MPC formulations by integrating multi-step
predictors, which provide reduced error bounds. These bounds, derived from
multi-step predictors, are utilized in a homothetic tube formulation to
mitigate conservatism. Lastly, a multi-rate formulation is adopted to handle
the incompatibilities of multi-step predictors. We provide a theoretical
analysis, guaranteeing robust recursive feasibility, constraint satisfaction,
and (practical) stability of the desired setpoint. We use a simulation example
to compare it to existing literature and demonstrate advantages in terms of
conservatism and computational complexity.
