Print Email Facebook Twitter Development of a Python tool based on model predictive control for an optimal management of the Calais canal Title Development of a Python tool based on model predictive control for an optimal management of the Calais canal Author Pour, Fatemeh Karimi (Institut Mines-Telecom) Duviella, Eric (Institut Mines-Telecom) Segovia Castillo, P. (TU Delft Transport Engineering and Logistics) Date 2022 Abstract Model predictive control (MPC) has been widely employed to control a large variety of water systems, such as dams, irrigation canals, inland waterways, drinking water networks and wastewater treatment plants. Its predictive capabilities and the possibility to incorporate constraints make MPC well suited to address several, and sometimes opposite, management objectives linked to water systems. The design of MPC for water systems is usually performed via dedicated software (e.g., Matlab) and tested in simulation using dedicated hydraulic software. However, the implementation of MPC strategies in real systems requires additional development to allow for its embedding within the information systems that are used by system managers. A possible solution is to create a tool based on Python that can be easily integrated with the information systems of managers, and within which existing Matlab solutions can be incorporated. In this paper, the development a ready-to-use Python tool using a hierarchical MPC approach designed for the management of the Calais Canal is presented. Subject Calais canalhierarchical controllarge-scale systemsmodel predictive controlPythonWater systems To reference this document use: http://resolver.tudelft.nl/uuid:f9b5f9ad-7dfb-4803-9ff9-f426510f1393 DOI https://doi.org/10.1016/j.ifacol.2022.11.001 ISSN 1474-6670 Source IFAC-PapersOnLine, 55 (33), 1-6 Event 2nd IFAC Workshop on Control Methods for Water Resource Systems, CMWRS 2022, 2022-09-22 → 2022-09-23, Milan, Italy Part of collection Institutional Repository Document type journal article Rights © 2022 Fatemeh Karimi Pour, Eric Duviella, P. Segovia Castillo Files PDF 1_s2.0_S2405896322026246_main.pdf 1009.9 KB Close viewer /islandora/object/uuid:f9b5f9ad-7dfb-4803-9ff9-f426510f1393/datastream/OBJ/view