  We propose a model predictive control (MPC) scheme with sampled-data input
which ensures output-reference tracking within prescribed error bounds for
relative-degree-one systems. Hereby, we explicitly deduce bounds on the
required maximal control input and sampling frequency such that the MPC scheme
is both initially and recursively feasible. A key feature of the proposed
approach is that neither terminal conditions nor a sufficiently-large
prediction horizon are imposed, rendering the MPC scheme computationally
efficient. We illustrate the MPC algorithm via a numerical example of a
torsional oscillator.
