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Reducing the Prediction Horizon in NMPC : An Algorithm Based Approach

DOI zum Zitieren der Version auf EPub Bayreuth: https://doi.org/10.15495/EPub_UBT_00005628
URN to cite this document: urn:nbn:de:bvb:703-epub-5628-4

Title data

Pannek, Jürgen ; Worthmann, Karl:
Reducing the Prediction Horizon in NMPC : An Algorithm Based Approach.
Bayreuth , 2011

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Abstract

In order to guarantee stability, known results for MPC without additional terminal costs or endpoint constraints often require rather large prediction horizons. Still, stable behavior of closed loop solutions can often be observed even for shorter horizons. Here, we make use of the recent observation that stability can be guaranteed for smaller prediction horizons via Lyapunov arguments if more than only the first control is implemented. Since such a procedure may be harmful in terms of robustness, we derive conditions which allow to increase the rate at which state measurements are used for feedback while maintaining stability and desired performance specifications. Our main contribution consists in developing two algorithms based on the deduced conditions and a corresponding stability theorem which ensures asymptotic stability for the MPC closed loop for significantly shorter prediction horizons.

Further data

Item Type: Preprint, postprint
Additional notes (visible to public): erscheint in:
IFAC Proceedings Volumes. Bd. 44 (2011) Heft 1 . - S. 7969-7974
DOI: https://doi.org/10.3182/20110828-6-IT-1002.00916
Keywords: model predictive control; stability; suboptimality estimate; algorithms; sampled
data system
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)
Faculties
Faculties > Faculty of Mathematics, Physics und Computer Science
Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Mathematics
Language: English
Originates at UBT: Yes
URN: urn:nbn:de:bvb:703-epub-5628-4
Date Deposited: 26 May 2021 13:20
Last Modified: 08 Jun 2021 07:45
URI: https://epub.uni-bayreuth.de/id/eprint/5628

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