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We propose a sampled-data Model Predictive Control (MPC) scheme withzero-order hold, which ensures output-reference tracking within predefinederror bounds for relative-degree-one systems. Hereby, we explicitly deducebounds on the required maximal control input and sampling frequency such thatthe MPC scheme is both initially and recursively feasible despite usingzero-order hold sampled-data control. A key feature of the proposed approach isthat neither terminal conditions nor a sufficiently-large prediction horizonare imposed, which renders the MPC scheme computationally very efficient.

Sampled-data Model Predictive Control with zero-order hold for arbitrary output constraints