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Model predictive sampled-data redesign for nonlinear systems

DOI zum Zitieren der Version auf EPub Bayreuth: https://doi.org/10.15495/EPub_UBT_00005522
URN to cite this document: urn:nbn:de:bvb:703-epub-5522-6

Title data

Grüne, Lars ; Nešić, Dragan ; Pannek, Jürgen:
Model predictive sampled-data redesign for nonlinear systems.
Bayreuth , 2005

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Abstract

We propose a model predictive control (MPC) strategy for sampled-data implementation (with the zero order hold assumption) of continuous-time controllers for general nonlinear systems. We assume that a continuous-time controller has been designed so that the continuous-time closed-loop satisfies all performance requirements. Then, we use this control law indirectly to compute numerically a sampled-data controller via an MPC strategy that minimizes the mismatch between the solutions of the sampled-data model and the continuous-time closed-loop model. We present conditions under which stability and sub-optimality of the closed loop can be proved, sketch the numerical implementation of our method and illustrate it by numerical simulations.

Further data

Item Type: Preprint, postprint
Additional notes (visible to public): Erscheint in: Proceedings of the 44th IEEE Conference on Decision and Control. - Piscatawny, NJ, USA : Institute of Electrical and Electronics Engineers , 2005 . - S. 36-41; https://doi.org/10.1109/CDC.2005.1582127
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-5522-6
Date Deposited: 17 May 2021 11:40
Last Modified: 17 Jun 2021 10:08
URI: https://epub.uni-bayreuth.de/id/eprint/5522
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