Titlebar

Export bibliographic data
Literature by the same author
plus on the publication server
plus at Google Scholar

 

Trajectory based suboptimality estimates for receding horizon controllers

DOI zum Zitieren der Version auf EPub Bayreuth: https://doi.org/10.15495/EPub_UBT_00005566
URN to cite this document: urn:nbn:de:bvb:703-epub-5566-9

Title data

Grüne, Lars ; Pannek, Jürgen:
Trajectory based suboptimality estimates for receding horizon controllers.
Bayreuth , 2008

[img]
Format: PDF
Name: gruene_pannek_mtns_2008.pdf
Version: Published Version
Available under License Creative Commons BY 4.0: Attribution
Download (166kB)

Abstract

In this paper we develop and illustrate methods for estimating the degree of suboptimality of receding horizon schemes with respect to infinite horizon optimal control. The proposed a posteriori and a priori methods yield estimates which are evaluated online along the computed closed-loop trajectories and only use numerical information which is readily available in the scheme.

Further data

Item Type: Preprint, postprint
Additional notes (visible to public): erscheint in:
Mathematical Theory of Networks and Systems (MTNS2008) : Proceedings of the 18th International Symposium Blacksburg, Virginia, USA. - Blacksburg, VA, USA , 2008
Keywords: nonlinear control; sampled-data; model predictive control; suboptimality
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-5566-9
Date Deposited: 20 May 2021 07:47
Last Modified: 10 Jun 2021 08:11
URI: https://epub.uni-bayreuth.de/id/eprint/5566

Downloads

Downloads per month over past year