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Dynamical tuning for MPC using population games: A water supply network application

AutorBarreiro-Gómez, Julian ; Ocampo-Martinez, Carlos; Quijano, Nicanor
Palabras claveDynamical tuning
Model predictive control
Game theory
Large-scale systems
Water supply networks
Fecha de publicación2017
EditorElsevier
CitaciónISA Transactions 69: 175-186 (2017)
ResumenModel 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.
Versión del editorhttps://doi.org/10.1016/j.isatra.2017.03.027
URIhttp://hdl.handle.net/10261/167068
Identificadoresdoi: 10.1016/j.isatra.2017.03.027
issn: 0019-0578
e-issn: 1879-2022
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