Irrigation control based on model predictive control (MPC): Formulation of theory and validation using weather forecast data and AQUACROP model

2016 ◽  
Vol 78 ◽  
pp. 40-53 ◽  
Author(s):  
Dilini Delgoda ◽  
Hector Malano ◽  
Syed K. Saleem ◽  
Malka N. Halgamuge
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Weather Forecast ◽  
Aquacrop Model ◽  
Forecast Data ◽  
Irrigation Control

scholarly journals Distributed tree-based model predictive control on a drainage water system

2012 ◽  
Vol 15 (2) ◽  
pp. 335-347 ◽  
Author(s):  
J. M. Maestre ◽  
L. Raso ◽  
P. J. van Overloop ◽  
B. De Schutter
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Drainage System ◽  
Water System ◽  
Open Water ◽  
Weather Forecast ◽  
Drainage Water ◽  
Programming Approach ◽  
Continuous Exposure ◽  
Dual Decomposition

Open water systems are one of the most externally influenced systems due to their size and continuous exposure to uncertain meteorological forces. The control of systems under uncertainty is, in general, a challenging problem. In this paper, we use a stochastic programming approach to control a drainage system in which the weather forecast is modeled as a disturbance tree. Each branch of the tree corresponds to a possible disturbance realization and has a certain probability associated to it. A model predictive controller is used to optimize the expected value of the system variables taking into account the disturbance tree. This technique, tree-based model predictive control (TBMPC), is solved in a distributed fashion. In particular, we apply dual decomposition to get an optimization problem that can be solved by different agents in parallel. In addition, different possibilities are considered in order to reduce the communicational burden of the distributed algorithm without reducing the performance of the controller significantly. Finally, the performance of this technique is compared with others such as minmax or multiple MPC.

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