Publication: Differentiation Tool Efficiency Comparison for Nonlinear Model Predictive Control Applied to Oil Gathering Systems
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Differentiation Tool Efficiency Comparison for Nonlinear Model Predictive Control Applied to Oil Gathering Systems

- Part of a collection -
 

Area
Control

Author(s)
A. Codas , M. A. S. Aguiar , K. Nalum , B. Foss

Published in
9th IFAC Symposium on Nonlinear Control Systems, Toulouse, France, September 4--6, 2013

Year
2013

Publisher
International Federation of Automatic Control

Abstract
This paper presents a comparison of gradient computation techniques required to solve a single-shooting formulation of nonlinear model predictive control (NMPC) problems. An oil production system with network structure is considered as test instance. The structure of the network is exploited to improve computational efficiency. Exact gradient sensitivity calculation methods (forward and adjoint) are compared along with the finite difference approximation. Forward and Reverse automatic differentiation for calculating Jacobians are also compared along with the finite difference approximation counterpart. Since there is a trade off involving accuracy and speed when calculating these gradients, the best combination of tools is case dependent and it is determined by the analyses of performance indexes arising when solving specific NMPC problems. A hybrid approach combining finite difference Jacobian calculations with adjoint sensitivity calculations gave the best performance for our test problems.

AD Tools
ADiMat

BibTeX
@INPROCEEDINGS{
         Codas2013DTE,
       author = "Codas, A. and Aguiar, M. A. S. and Nalum, K. and Foss, B.",
       title = "Differentiation Tool Efficiency Comparison for Nonlinear Model Predictive Control
         Applied to Oil Gathering Systems",
       booktitle = "9th IFAC Symposium on Nonlinear Control Systems, Toulouse, France, September 4--6,
         2013",
       year = "2013",
       pages = "821--826",
       doi = "10.3182/20130904-3-FR-2041.00069",
       publisher = "International Federation of Automatic Control",
       abstract = "This paper presents a comparison of gradient computation techniques required to
         solve a single-shooting formulation of nonlinear model predictive control (NMPC) problems. An oil
         production system with network structure is considered as test instance. The structure of the
         network is exploited to improve computational efficiency. Exact gradient sensitivity calculation
         methods (forward and adjoint) are compared along with the finite difference approximation. Forward
         and Reverse automatic differentiation for calculating Jacobians are also compared along with the
         finite difference approximation counterpart. Since there is a trade off involving accuracy and speed
         when calculating these gradients, the best combination of tools is case dependent and it is
         determined by the analyses of performance indexes arising when solving specific NMPC problems. A
         hybrid approach combining finite difference Jacobian calculations with adjoint sensitivity
         calculations gave the best performance for our test problems.",
       ad_area = "Control",
       ad_tools = "ADiMat"
}


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