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We propose a model predictive control (MPC) scheme with sampled-data inputwhich ensures output-reference tracking within prescribed error bounds forrelative-degree-one systems. Hereby, we explicitly deduce bounds on therequired maximal control input and sampling frequency such that the MPC schemeis both initially and recursively feasible. A key feature of the proposedapproach is that neither terminal conditions nor a sufficiently-largeprediction horizon are imposed, rendering the MPC scheme computationallyefficient. We illustrate the MPC algorithm via a numerical example of atorsional oscillator.

On model predictive control with sampled-data input for output tracking with prescribed performance