IAHR, founded in 1935, is a worldwide independent member-based organisation of engineers and water specialists working in fields related to the hydro-environmental sciences and their practical application. Activities range from river and maritime hydraulics to water resources development and eco-hydraulics, through to ice engineering, hydroinformatics, and hydraulic machinery.
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You are here : eLibrary : IAHR World Congress Proceedings : 36th Congress - The Hague (2015) ALL CONTENT : Water resources and hydroinformatics : Achieving offset free model predictive control of irrigation canals
Achieving offset free model predictive control of irrigation canals
Model predictive control (MPC) is a powerful control option which is increasingly used by operational water managers for managing water systems. However, these applications on water systems have offset problem because of the mismatch between the models used in MPC and the actual system. The main reasons of this mismatch are the unknown disturbances and other uncertainties present in the system, which cannot be modelled. Mismatch results in offset which
prevents MPC to achieve its goal of reaching the set point for the controlled variable. This article shows two different methods to achieve offset free MPC of the first pool of the laboratory canal at Technical University of Catalonia, Barcelona. First method is modelling the disturbance dynamics of the system by means of a disturbance model. An integrating disturbance is augmented into the internal model as an additional state and a kalman filter is designed to update the states of the system using the measurements. The second method is developed and tested by the writers
using the Moving Horizon State Estimation (MHE) method to estimate the unknown disturbances affecting the system.
These estimated disturbances are used during the optimization phase of the MPC to improve the overall control performance of the MPC and achieve offset free control. Simulation results of the two methods are provided and the results are used to compare the two methods. The results of the second method shows that estimation of unknown disturbances using MHE is a powerful way to remove the offset in the controlled variables.
File Size : 173,352 bytes
File Type : Adobe Acrobat Document
Chapter : IAHR World Congress Proceedings
Category : 36th Congress - The Hague (2015) ALL CONTENT
Article : Water resources and hydroinformatics
Date Published : 01/10/2015
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