Cooperative Linker for the Distributed Control of the Barcelona Drinking Water Network

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published 86899 2 246787 document 86899 lightbox.jpg image/png b0290e7b91a082e564eea680c0dd49b1 MD5 41755 2019-03-12 10:00:44 https://pure.iiasa.ac.at/id/eprint/15791/8/lightbox.jpg 15791 8 8 image/png other Thumbnails conversion from text to thumbnail_lightbox en public

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/86898 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/86898 http://eprints.org/relation/isIndexCodesVersionOf https://pure.iiasa.ac.at/id/document/86898 archive 353 disk0/00/01/57/91 2019-03-12 07:32:39 2023-02-01 05:00:34 2019-03-12 07:32:39 book_section show Javalera Rincón V. 2170 0000-0001-8743-9777 Cayuela V.P. Seix B.M. Orduña-Cabrera F. 2239 0000-0002-8558-0053 Cooperative Linker for the Distributed Control of the Barcelona Drinking Water Network pub prog_asa prog_esm Multi-Agent Systems, Large Scale Systems, Linkage ofModels, Reinforcement Learning, Distributed Control, Water Networks, Large Scale Systems This work shows how a Linker agent coordinates a cooperative MAS environment to seek a global optimum. The approach is applied to the Barcelona Drinking Water Network (DWN) administrated by AGBAR where the main problem was to coordinate the control of three different sectors of the network. Each part has a local controller (local agent) to solve the local water demands, but it also has to cooperate with the other agents to satisfy the water demands of the whole network. The cooperative Linker agent implemented, learns by using a Reinforcement Learning algorithm, called PlanningByExploration Behaviour with penalization (Javalera et al., 2019), to converge towards an optimal (or suboptimal) value of each of the variables that connect the local agents. For the training and simulation of the Linker agents real historical data of the Barcelona DWN provided by AGBAR were used, as well as the data to model the distributed topology of the DWN. Moreover, some results of the simulations of this approach in contrast with the results of a centralized Model Predictive Controller are depicted. 2019 published ICAART 10.5220/0007349105600567 http://www.icaart.org/ 3030 3130 public 2 Porto, Portugal 560-567 TRUE 978-989-758-350-6 Proceedings of the 11th International Conference on Agents and Artificial Intelligence (ICAART 2019) FALSE no info:eu-repo/semantics/bookPart