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

Fuente: arXiv
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Main Authors: Dennstädt, Dario, Lanza, Lukas, Worthmann, Karl
Format: Preprint
Published: 2023
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author Dennstädt, Dario
Lanza, Lukas
Worthmann, Karl
author_facet Dennstädt, Dario
Lanza, Lukas
Worthmann, Karl
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2312_07394
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle On model predictive control with sampled-data input for output tracking with prescribed performance
Dennstädt, Dario
Lanza, Lukas
Worthmann, Karl
Optimization and Control
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.
title On model predictive control with sampled-data input for output tracking with prescribed performance
topic Optimization and Control
url https://arxiv.org/abs/2312.07394