DocumentCode :
2582769
Title :
A convex approach for NMPC based on second order Volterra series models
Author :
Gruber, J.K. ; Alamo, T. ; Ramírez, D.R. ; Bordons, C. ; Camacho, E.F.
Author_Institution :
Dept. de Ing. de Sist. y Autom., Univ. of Seville, Seville, Spain
fYear :
2010
fDate :
15-17 Dec. 2010
Firstpage :
1336
Lastpage :
1341
Abstract :
This paper presents a novel approach to use second order Volterra series models in nonlinear model predictive control. A common technique in model predictive control is the minimization of a quadratic cost function with respect to the future input sequence. In the case of nonlinear models, the resulting cost function is a possibly non-convex function. The proposed strategy uses quadratic cost functions to approximate the original cost function. For the quadratic cost functions, convexity can be achieved easily by adding a weighting function of the control increments. The approximated convex cost functions are minimized globally by means of an iterative approach with guaranteed convergence. The proposed control strategy is applied to a continuous stirred tank reactor and the control performance is illustrated by experimental results.
Keywords :
Volterra series; convergence; convex programming; iterative methods; minimisation; nonlinear control systems; predictive control; NMPC; continuous stirred tank reactor; convergence; convex cost functions; iterative approach; minimization; nonlinear model predictive control; quadratic cost function; second order Volterra series models; Accuracy; Approximation methods; Computational modeling; Convergence; Cost function; Predictive models;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Decision and Control (CDC), 2010 49th IEEE Conference on
Conference_Location :
Atlanta, GA
ISSN :
0743-1546
Print_ISBN :
978-1-4244-7745-6
Type :
conf
DOI :
10.1109/CDC.2010.5718065
Filename :
5718065
Link To Document :
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