Title :
Probabilistically robust model predictive control
Author_Institution :
Dept. of C SEP, Univ. of Michigan-Flint, Flint, MI, USA
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;
Conference_Titel :
Control, Automation and Systems (ICCAS), 2013 13th International Conference on
Conference_Location :
Gwangju
Print_ISBN :
978-89-93215-05-2
DOI :
10.1109/ICCAS.2013.6704059
