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- W2766763710 abstract "Abstract Large-scale systems involve a high number of variables making challenging the design of controllers because of information availability and computational burden issues. Normally, the measurement of all the states in a large-scale system implies to have a big communication network, which might be quite expensive. On the other hand, the treatment of large amount of data to compute the appropriate control inputs implies high computational costs. An alternative to mitigate the aforementioned issues is to split the problem into several sub-systems. Thus, computational tasks may be split and assigned to different local controllers, letting to reduce the required time to compute the control inputs. Additionally, the partitioning of the system allows control designers to simplify the communication network. This paper presents a partitioning algorithm performed by considering an information-sharing graph that can be generated for any control strategy and for any dynamical large-scale system. Finally, a distributed model predictive control (DMPC) is designed for a large-scale system as an application of the proposed partitioning method." @default.
- W2766763710 created "2017-11-10" @default.
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- W2766763710 date "2017-07-01" @default.
- W2766763710 modified "2023-10-18" @default.
- W2766763710 title "Partitioning for Large-scale Systems: A Sequential Distributed MPC Design * *This work has been partially supported by the project DEOCS (Ref. DPI2016-76493-C3-3-R). J. Barreiro-Gomez is partially supported by Colciencias and AGAUR." @default.
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- W2766763710 doi "https://doi.org/10.1016/j.ifacol.2017.08.1539" @default.
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