DocumentCode
2252093
Title
Constrained NMPC via state-space partitioning for input-affine non-linear systems
Author
Bacic, Marko ; Cannon, Mark ; Kouvaritakis, Basil
Author_Institution
Dept. of Eng. Sci., Oxford Univ., UK
Volume
6
fYear
2003
fDate
4-6 June 2003
Firstpage
4881
Abstract
State-space partitioning and judicious use of graph theory is deployed to propose a novel nonlinear model predictive control (NMPC) approach that is suitable for fast sampling applications. The efficacy of the approach is demonstrated by means of a design study.
Keywords
graph theory; nonlinear systems; predictive control; sampling methods; state-space methods; constrained NMPC; fast sampling application; graph theory; input-affine nonlinear system; nonlinear model predictive control; nonlinear optimization; state-space partitioning; Computational efficiency; Constraint theory; Costs; Graph theory; Infinite horizon; Optimal control; Partitioning algorithms; Predictive control; Sampling methods; Stability;
fLanguage
English
Publisher
ieee
Conference_Titel
American Control Conference, 2003. Proceedings of the 2003
ISSN
0743-1619
Print_ISBN
0-7803-7896-2
Type
conf
DOI
10.1109/ACC.2003.1242496
Filename
1242496
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