Reference
M. D. Doan, P. Giselsson, T. Keviczky, B.
De Schutter, and A. Rantzer, "A distributed accelerated gradient algorithm for
distributed model predictive control of a hydro power valley,"
Control Engineering Practice, vol. 21, no. 11, pp. 1594-1605,
Nov. 2013.
Abstract
A distributed model predictive control (DMPC) approach based on distributed
optimization is applied to the power reference tracking problem of a hydro
power valley (HPV) system. The applied optimization algorithm is based on
accelerated gradient methods and achieves a convergence rate of
O(1/k
2), where k is the iteration number. Major challenges in the
control of the HPV include a nonlinear and large-scale model, nonsmoothness in
the power-production functions, and a globally coupled cost function that
prevents distributed schemes to be applied directly. We propose a linearization
and approximation approach that accommodates the proposed the DMPC framework
and provides very similar performance compared to a centralized solution in
simulations. The provided numerical studies also suggest that for the sparsely
interconnected system at hand, the distributed algorithm we propose is faster
than a centralized state-of-the-art solver such as CPLEX.
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BibTeX
@article{DoaGis:13-027,
author = {Doan, Minh Dang and Giselsson, Pontus and Keviczky, Tam{\'a}s and
De Schutter, Bart and Rantzer, Anders},
title = {A Distributed Accelerated Gradient Algorithm for Distributed
Model Predictive Control of a Hydro Power Valley},
journal = {Control Engineering Practice},
volume = {21},
number = {11},
pages = {1594--1605},
month = nov,
year = {2013}
}