Publication
Learning-based tuning of supervisory model predictive control for drinking water networks
Journal Article (2013)
Journal
Engineering Applications of Artificial Intelligence
Pages
1741-1750
Volume
26
Number
7
Doc link
http://dx.doi.org/10.1016/j.engappai.2013.03.003
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Authors
Projects associated
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.
Categories
control system synthesis, control theory, fuzzy control, optimisation, predictive control.
Author keywords
predictive control, learning, drinking water networks, industrial applications, tuning of controllers
Scientific reference
J.M. Grosso, C. Ocampo-Martínez and V. Puig. Learning-based tuning of supervisory model predictive control for drinking water networks. Engineering Applications of Artificial Intelligence, 26(7): 1741-1750, 2013.
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