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Model predictive control for nonlinear sampled-data systems

DOI zum Zitieren der Version auf EPub Bayreuth: https://doi.org/10.15495/EPub_UBT_00005549
URN to cite this document: urn:nbn:de:bvb:703-epub-5549-9

Title data

Grüne, Lars ; Nešić, Dragan ; Pannek, Jürgen:
Model predictive control for nonlinear sampled-data systems.
Bayreuth , 2007

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Abstract

The topic of this paper is a new model predictive control (MPC) approach for the sampled-data implementation of continuous-time stabilizing feedback laws. The given continuous-time feedback controller is used to generate a reference trajectory which we track numerically using a sampled-data controller via an MPC strategy. Here our goal is to minimize the mismatch between the reference solution and the trajectory under control. We summarize the necessary theoretical results, discuss several aspects of the numerical implemenation and illustrate the algorithm by an example.

Further data

Item Type: Preprint, postprint
Additional notes (visible to public): Erscheint in: Findeisen, Rolf ; Allgöwer, Frank ; Biegler, Lorenz T. (Hrsg.): Assessment and Future Directions of Nonlinear Model Predictive Control. - Berlin ; Heidelberg : Springer , 2007 . - S. 105-113
Keywords: nonlinear control; sampled-data; model predictive control; reference solution
DDC Subjects: 500 Science
500 Science > 510 Mathematics
Institutions of the University: Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics > Chair Mathematics V (Applied Mathematics) > Chair Mathematics V (Applied Mathematics) - Univ.-Prof. Dr. Lars Grüne
Faculties
Faculties > Faculty of Mathematics, Physics und Computer Science
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics > Chair Mathematics V (Applied Mathematics)
Language: English
Originates at UBT: Yes
URN: urn:nbn:de:bvb:703-epub-5549-9
Date Deposited: 19 May 2021 06:45
Last Modified: 15 Jun 2021 09:17
URI: https://epub.uni-bayreuth.de/id/eprint/5549

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