DocumentCode
669493
Title
Probabilistically robust model predictive control
Author
Young-Man Kim
Author_Institution
Dept. of C SEP, Univ. of Michigan-Flint, Flint, MI, USA
fYear
2013
fDate
20-23 Oct. 2013
Firstpage
987
Lastpage
990
Abstract
This paper presents probabilistically robust controller design technique based on model predictive control theory. Recent probabilistic robust control theory allows us to design controller without any restriction on the structure of uncertainties. Model predictive control theory is useful for dealing with constraints on design parameter. Subgradient optimization technique is used to find Suboptimal.
Keywords
control system synthesis; gradient methods; optimisation; predictive control; probability; robust control; uncertain systems; model predictive control theory; probabilistically robust controller design technique; subgradient optimization technique; uncertainties structure; Matrix decomposition; Predictive models; Probabilistic logic; Robustness; Upper bound; Linear Matrix Inequalities (LMI); Model Predictive Control; Probabilistic Robustness; Subgradient Optimization;
fLanguage
English
Publisher
ieee
Conference_Titel
Control, Automation and Systems (ICCAS), 2013 13th International Conference on
Conference_Location
Gwangju
ISSN
2093-7121
Print_ISBN
978-89-93215-05-2
Type
conf
DOI
10.1109/ICCAS.2013.6704059
Filename
6704059
Link To Document