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arXiv · 2312.07394

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

Dario Dennstädt·Lukas Lanza·Karl Worthmann
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Abstract

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.

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Dario Dennstädt, Lukas Lanza, Karl Worthmann. 2023-12-12. On model predictive control with sampled-data input for output tracking with prescribed performance. https://arxiv.org/abs/2312.07394

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