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.
CitationGrosso, J.; Ocampo-Martinez, C.A.; Puig, V. Learning-based tuning of supervisory model predictive control for drinking water networks. "Engineering applications of artificial intelligence", 2013, vol. 26, núm. 7, p. 1741-1750.
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