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


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/k2), 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 entry:

@article{DoaGis:13-027,
        author={M.D. Doan and P. Giselsson and T. Keviczky and B. {D}e Schutter and A. Rantzer},
        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},
        doi={10.1016/j.conengprac.2013.06.012}
        }



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