DocumentCode :
3693467
Title :
First-order methods in embedded nonlinear model predictive control
Author :
D. Kouzoupis;H. J. Ferreau;H. Peyrl;M. Diehl
Author_Institution :
Faculty of Microsystems Engineering, Albert Ludwigs University of Freiburg, Germany
fYear :
2015
fDate :
7/1/2015 12:00:00 AM
Firstpage :
2617
Lastpage :
2622
Abstract :
Several algorithms based on Nesterov´s fast gradient method have been recently proposed in the literature for use in linear model predictive control (MPC). Their simple algorithmic schemes have attracted much attention for MPC applications on embedded hardware. The purpose of this paper is to investigate the suitability of these algorithms in a nonlinear MPC setup. We assess the necessary numerical modifications and analyze the additional online computational complexity. We illustrate our findings by combining different first-order methods with the real-time iteration (RTI) scheme for nonlinear MPC and using them to control a model of an inverted pendulum.
Keywords :
"Gradient methods","Heuristic algorithms","Prediction algorithms","Linear programming","Computational modeling","Convergence"
Publisher :
ieee
Conference_Titel :
Control Conference (ECC), 2015 European
Type :
conf
DOI :
10.1109/ECC.2015.7330932
Filename :
7330932
Link To Document :
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