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Title:
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Dynamical tuning for MPC using population games: a water supply network application
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Author:
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Barreiro Gómez, Julián; Ocampo-Martínez, Carlos; Quijano Silva, Nicanor
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Other authors:
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Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial; Universitat Politècnica de Catalunya. SAC - Sistemes Avançats de Control |
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Abstract:
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ISA Transactions Best Paper Award 2018 |
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Abstract:
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Model predictive control (MPC) is a suitable strategy for the control of large-scale systems that have multiple design requirements, e.g., multiple physical and operational constraints. Besides, an MPC controller is able to deal with multiple control objectives considering them within the cost function, which implies to determine a proper prioritization for each of the objectives. Furthermore, when the system has time-varying parameters and/or disturbances, the appropriate prioritization might vary along the time as well. This situation leads to the need of a dynamical tuning methodology. This paper addresses the dynamical tuning issue by using evolutionary game theory. The advantages of the proposed method are highlighted and tested over a large-scale water supply network with periodic time-varying disturbances. Finally, results are analyzed with respect to a multi-objective MPC controller that uses static tuning. |
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Abstract:
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Peer Reviewed |
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Abstract:
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Award-winning |
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Subject(s):
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-Àrees temàtiques de la UPC::Informàtica::Automàtica i control
-Dynamical tuning
-Model predictive control
-Game theory
-Large-scale systems
-Water supply networks
-Classificació INSPEC::Control theory::Predictive control |
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Rights:
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©2017. Elsevier
Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
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Document type:
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Article - Submitted version
Article |
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