Learning-based tuning of supervisory model predictive control for drinking water networks |
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Authors: | J.M. Grosso C. Ocampo-Martínez V. Puig |
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Affiliation: | Institut de Robòtica i Informàtica Industrial (CSIC-UPC), Llorens i Artigas 4-6, 08028 Barcelona, Spain |
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Abstract: | This paper presents a constrained Model Predictive Control (MPC) strategy enriched with soft-control techniques as neural networks and fuzzy logic, to incorporate self-tuning capabilities and reliability aspects for the management of drinking water networks (DWNs). The control system architecture consists in a multilayer controller with three hierarchical layers: learning and planning layer, supervision and adaptation layer, and feedback control layer. Results of applying the proposed approach to the Barcelona DWN show that the quasi-explicit nature of the proposed adaptive predictive controller leads to improve the computational time, especially when the complexity of the problem structure can vary while tuning the receding horizons. |
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Keywords: | Model predictive control Self-tuning Multilayer controller Neural networks Fuzzy-logic Drinking water networks |
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