A distributed accelerated gradient algorithm for distributed model predictive control of a hydro power valley

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Bibtex

@article{9650c8d916ef47cf9d3aeb379e753856,
title = "A distributed accelerated gradient algorithm for distributed model predictive control of a hydro power valley",
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, non-smoothness 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.",
keywords = "Distributed optimization, Hydro power control, Accelerated gradient algorithm, Distributed model predictive control, Model predictive control",
author = "Doan, {Minh Dang} and Pontus Giselsson and Tam{\'a}s Keviczky and {De Schutter}, Bart and Anders Rantzer",
year = "2013",
doi = "10.1016/j.conengprac.2013.06.012",
language = "English",
volume = "21",
pages = "1594--1605",
journal = "Control Engineering Practice",
issn = "0967-0661",
publisher = "Elsevier",
number = "11",

}